ORTHOGONAL TIME FREQUENCY SPACE MODULATION SYSTEM FOR THE INTERNET OF THINGS

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First Claim
1. A method of operating an IOT device, the method comprising:
 determining, during operation in a low power mode, an OTFS transmission waveform using twodimensional channel state information wherein the twodimensional (2D) channel state information is with respect to a delayDoppler channel domain; and
transmitting, during operation in a high power mode, the OTFS transmission waveform.
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Abstract
A system and method of operating an Internet of Things (IOT) device and an IOT manager device. The method includes determining, during operation of the IOT device in a low power mode, an orthogonal time frequency space (OTFS) transmission waveform using twodimensional (2D) channel state information relevant to a delayDoppler channel domain. The method further includes transmitting, during operation of the IOT device in a high power mode, the OTFS transmission waveform. The process of determining the OTFS transmission waveform may include, for example, receiving, from the IOT manager device, the 2D channel state information and storing it within a memory of the IOT device. Alternatively, at least one OTFS pilot transmission may be received from the IOT manager device and the 2D channel state information determined using the OTFS pilot transmission.
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No References
37 Claims
 1. A method of operating an IOT device, the method comprising:
determining, during operation in a low power mode, an OTFS transmission waveform using twodimensional channel state information wherein the twodimensional (2D) channel state information is with respect to a delayDoppler channel domain; and transmitting, during operation in a high power mode, the OTFS transmission waveform.  View Dependent Claims (2, 3, 4)
 5. A method of operating an IOT device, the method comprising:
operating a processor of the IOT device in a low power mode; transitioning operation of the processor to a high power mode; transmitting an OTFS waveform during the operation of the processor in the high power mode; and transitioning, after the transmitting, the processor to operation in the low power mode.  View Dependent Claims (6, 7, 8, 9, 10)
 11. A method of operating an IOT device, the method comprising:
selecting an OTFS data symbol in response to an input signal; generating an OTFS waveform using the OTFS data symbol and twodimensional (2D) channel state information wherein the OTFS data symbol is included within an OTFS data frame and wherein the IOT device is included among a plurality of IOT devices uniquely associated with locations within the OTFS data frame; and transmitting the OTFS waveform.  View Dependent Claims (12, 13, 14, 15)
 16. A method for operating an IOT manager device in wireless communication with a plurality of IOT devices, the method comprising:
receiving, from the plurality of IOT devices, a corresponding plurality of sets of OTFS pilot waveforms; determining, based upon the plurality of sets of OTFS pilot waveforms, a corresponding plurality of twodimensional (2D) channel states; and transmitting information relating to the plurality of 2D channel states to corresponding ones of the plurality of IOT devices.  View Dependent Claims (17, 18, 19)
 20. A method for operating an IOT manager device in wireless communication with a plurality of IOT devices, the method comprising:
transmitting OTFS pilot waveforms generated using OTFS pilot symbols occupying locations within an OTFS data frame reserved for the pilot symbols; and receiving a plurality of OTFS data transmissions from the plurality of IOT devices wherein the plurality of OTFS data transmissions are generated using a corresponding plurality of OTFS data symbols associated with a plurality of locations in the OTFS data frame respectively corresponding to the plurality of IOT devices, the plurality of locations being different from the locations within the OTFS data frame reserved for the pilot symbols.
 21. An IOT device, comprising:
a transceiver; a processor capable of operating in a low power mode and in a high power mode; and a memory including program code executable by the processor, the program code including; code for determining, during operation of the processor in the low power mode, an OTFS transmission waveform using twodimensional channel state information wherein the twodimensional (2D) channel state information is with respect to a delayDoppler channel domain; code for causing the transceiver to transmit, during operation of the processor in the high power mode, the OTFS transmission waveform.  View Dependent Claims (22, 23, 24)
 25. An IOT device, comprising:
a wireless transceiver; a processor capable of operating in a low power mode and in a high power mode; and a memory including program code executable by the processor, the program code including; code for causing the processor to transition from operation in the low power mode to operation in the high power mode; code for causing the wireless transceiver to transmit an OTFS waveform during the operation of the IOT device in the high power mode; code for causing processor to transition, after the transmitting, to operation in the low power mode.  View Dependent Claims (26, 27, 28)
 29. An IOT device, comprising:
a wireless transceiver; a processor; and a memory including program code executable by the processor, the program code including; code for selecting an OTFS data symbol in response to an input signal; code for generating an OTFS waveform using the OTFS data symbol and twodimensional (2D) channel state information wherein the OTFS data symbol is included within an OTFS data frame and wherein the IOT device is included among a plurality of IOT devices uniquely associated with locations within the OTFS data frame; code for causing the wireless transceiver to transmit the OTFS waveform.  View Dependent Claims (30, 31, 32)
 33. An IOT manager device in wireless communication with a plurality of IOT devices, the IOT manager device comprising:
a transceiver configured to receive, from the plurality of IOT devices, a corresponding plurality of sets of OTFS pilot waveforms; a processor; a memory including program code executable by the processor, the program code including; code for determining, based upon the plurality of sets of OTFS pilot waveforms, a corresponding plurality of twodimensional (2D) channel states; code for causing the transceiver to transmit information relating to the plurality of 2D channel states to corresponding ones of the plurality of IOT devices.  View Dependent Claims (34, 35, 36)
 37. An IOT manger device in wireless communication with a plurality of IOT devices, the IOT manager device comprising:
a transceiver; a processor; and a memory including program code executable by the processor, the program code including; code for causing the transceiver to transmit OTFS pilot waveforms generated using OTFS pilot symbols occupying locations within an OTFS data frame reserved for the pilot symbols; and code for decoding a plurality of OTFS data transmissions received by the transceiver from the plurality of IOT devices wherein the plurality of OTFS data transmissions are generated using a corresponding plurality of OTFS data symbols associated with a plurality of locations in the OTFS data frame respectively corresponding to the plurality of IOT devices, the plurality of locations being different from the locations within the OTFS data frame reserved for the pilot symbols.
1 Specification
The present application claims the benefit of priority under 35 U.S.C. §119(e) of U.S. Provisional Application No. 62/154,937, entitled SYSTEM AND METHOD TO PRODUCE AN OTFS WIRELESS CONNECTED INTERNET OF THINGS, filed Apr. 30, 2015, and of U.S. Provisional Application No. 62/215,663, entitled ORTHOGONAL TIME FREQUENCY SPACE COMMUNICATION SYSTEM AND METHOD, filed Sep. 8, 2015, the contents of each of which are hereby incorporated by reference in their entirety for all purposes. The present application is a continuationinpart of U.S. application Ser. No. 14/709,377, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed May 11, 2015, which is a continuation of U.S. patent application Ser. No. 13/927,086, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed on Jun. 25, 2013, which claims the benefit of priority under 35 U.S.C. §119(e) of U.S. Provisional Application Ser. No. 61/664,020, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Jun. 25, 2012, of U.S. Provisional Application Ser. No. 61/801,398, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, of U.S. Provisional Application Ser. No. 61/801,366, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, of U.S. Provisional Application Ser. No. 61/801,435, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, of U.S. Provisional Application Ser. No. 61/801,495, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, of U.S. Provisional Application Ser. No. 61/801,994, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, and of U.S. Provisional Application Ser. No. 61/801,968, entitled MODULATION AND EQUALIZATION IN AN ORTHONORMAL TIMEFREQUENCY SHIFTING COMMUNICATIONS SYSTEM, filed Mar. 15, 2013, the contents of each of which are hereby incorporated by reference in their entirety for all purposes.
This disclosure generally relates to wireless communication protocols and methods, and more particularly relates to methods for wireless communication involving devices useable within the Internet of Things.
In recent years, there has been a large amount of interest in providing computer network connectivity, in particular Internet connectivity, to nearly every conceivable device. This type of effort has been commonly been termed the “Internet of Things” (abbreviated as either IoT or IOT). Indeed, the IOT is commonly understood to be a concept, system, or method, by which everyday objects may be connected to computer networks such as the Internet. Each IOT device or “thing” may be uniquely identifiable through its embedded computing system and is able to interoperate within the existing Internet infrastructure. In many applications, the IOT devices used to provide IOT functionality will often have very limited access to electrical energy and often must be relatively small and inexpensive.
Although in principle any device, including large devices directly connected to high capacity energy sources and equipped with high power consumption processors, can be connected to the Internet, the term IOT is often used in conjunction with devices that may have little or no access to energy beyond builtin battery energy or other limited energy sources. Often, these limited energy sources are expected to operate the IOT device for periods of years or more.
It is also envisioned that many IOT devices, in particular IOT sensors, should be able to operate over relatively long periods of time with little or no need for Internet or other computer network connectivity. That is, a typical IOT device may often have long periods of low activity and low communications needs, followed by occasional periods where higher activity and higher communications needs may be required.
To reduce power and energy demands, processors commonly used for IOT functions, such as processors from the popular ARM family of processors, typically can be configured to operate at a variety of different clock speeds and to shut down some or all portions of its processor core when not all of processing capability is needed. These various processor states can save a considerable amount of energy because, to a first approximation, processor power use (e.g. energy use per unit time) is often proportional to the processor'"'"'s clock speed, which is the frequency of operation of the processor'"'"'s central processing unit (CPU). Certain processors may be configured to operate in various power consumption states, such as in (i) an ultralow power consumption state where only basic timekeeping circuits and/or interrupt wakeup circuits may be activated, (ii) a low power state, where the processor is fully functioning but operating at perhaps ⅕ to 1/10 or less of its highest clock speed and power needs, and (iii) a high power consumption state where the processor is operating at or near its highest clock speed. Typically such processors will also have timing or clock circuitry that can be configured to “wake up” the processor at various intervals, and cause the processor to transition, for example, from an ultralow to low power mode, or from lowpower mode to high power mode. The processor can spend most of its time idling at a lower power consumption mode, and only transition to high power consumption mode when needed.
Although some IOT devices may connect to larger computer networks, such as the internet, with hardwire network connections such as electrical wires or cables, optical fiber, and the like, for many potential IOT devices, wireless network connection methods is often either preferred, or indeed may be the only feasible method. Here prior art wireless connectivity schemes, such as the popular IEEE 802.11 (WiFi) methods, IEEE 802.15 (Bluetooth™) methods, IEEE. 802.15.4 (ZigBee™) methods, Zwave™ wireless mesh methods, and the like may be used. Wireless mesh network schemes can help enable the relatively low power IOT wireless signals to be transmitted from various IOT devices to various Internet access points.
However these prior art wireless methods tend to have both a significant amount of both power consumption and computational overhead associated with them. Additionally, because such prior art wireless methods can be easily disrupted due to various types of channel imperfections, it is often necessary to expend yet more energy and overhead in encoding, verifying, and retransmitting information packets in order to cope with such disruptions.
The present disclosure is based, in part, upon the insight that the usefulness and performance of IOT devices may be improved by utilizing orthogonal time frequency space (OTFS) modulation techniques, typically on an occasional basis and in accordance with various powersparing modes of operation, in order to facilitate communication with IOT devices.
The present disclosure is also based, in part, on the insight that the wireless mesh networks often used provide network connectivity to IOT devices suffer from a number of drawbacks. For example, in order to maintain such mesh networks, which are composed of other wirelessly connected IOT devices, such other IOT devices are usually required to wake up, listen for signals, and report in at a certain rate. This requires that every IOT device in the mesh network regularly expend energy. By contrast, OTFS methods reduce the need for such mesh networks. This is because the unique channel state mitigating and compensation aspects of the OTFS methods described herein allow IOT devices to transmit wireless signals directly to a wireless base station using similar amounts of energy as are typically required for the far shorter transmission distances between mesh network nodes. By allowing longer distance transmissions on the same or lower energy budget that prior art IOT devices use for shorter range communications, OTFS methods can reduce or eliminate the need for wireless IOT devices to support the overhead of a mesh network and can minimize the need for access to energy sources.
Nonetheless, the OTFS methods described herein may be computationally intensive. Thus, in certain embodiments the energy demands associated with OTFS communication can be reduced by, for example, capturing, digitizing and storing OTFS waveforms while running in higher processor power modes. Because many IOT devices are idling in lower processor power modes for much of the time, it may often then be feasible to slowly (often while the processors running in a slower, lowerpower mode, then interpret the stored waveforms and related information. Similarly, in the case of transmission waveforms, it often may be feasible to, while the processor may be running in a slower, lower power mode, to slowly generate OTFS waveforms intended for transmission and store these OTFS waveforms in the IOT device memory in a digital form. Then, when transmission is desired, the IOT device processor may transition into a higher power mode, rapidly convert (e.g. using a A/D converter) these digital waveforms into an analog form and transmit them.
Such novel OTFS IOT devices may also impose novel device management and control demands. Accordingly, the present disclosure also describes a new type of wireless IOT manager device as well as schemes for managing the IOT devices. For example, it will generally be necessary or desirable to provide an Internet address or “ID” for each IOT device, such as an IPv4 or IPv6 address, Universal Resource Identifier (URI), Universal Resource Locator (URL0), and the like. In one implementation the IOT device may itself, of course, incorporate an IPv4 or IPv6 or other URI/URL type address and only communicate and respond using this type address. However, for higher efficiency an IOT device operating under the control of an IOT manager device might communicate with the IOT manager device using a shorter address. The IOT manger device may, for example, act to translate between the IOT device'"'"'s shorter address and the longer and more standard IPv4 or IPv6 or other URI/URL addresses. This requires less overhead and energy than would typically be required to perform addressing operations using conventional wireless communication protocols.
Thus, in one aspect the present disclosure pertains to a wireless OTFS system, IOT device system, or method of connecting at least one low power IOT device to at least one wireless IOT manager device over an impaired wireless data channel. The IOT device can comprise at least one processor configured to operate in a plurality of power modes, such as at least a lower power mode and a higher power mode. The IOT device can also comprise memory, at least one timer device, and at least one clock controlled by the timer device. The IOT device may be associated with an IOT device identification or ID. A wireless transceiver of the IOT device will generally be capable of operating at least when the processor of the IOT device is running in a higher power mode.
The internal clock of the IOT device may “wake up” or otherwise configure the processor(s) of the IOT device to transition from a lower power mode having less computational capability to a higher power mode having relatively higher computational capability. In this higher power mode the IOT device may, for example, wirelessly transmit various OTFS pilot bursts and/or OTFS data symbol waveform bursts to various OTFS receivers to an IOT manager device.
In another aspect the disclosure relates to a method of operating an IOT device. The method includes determining, during operation in a low power mode, an OTFS transmission waveform using twodimensional (2D) channel state information relevant to a delayDoppler channel domain. The method further includes transmitting, during operation in a high power mode, the OTFS transmission waveform. The process of determining the OTFS transmission waveform may include, for example, receiving, from an IOT manager device, the 2D channel state information and storing it within a memory. Alternatively, at least one OTFS pilot transmission may be received from the IOT manager device and the 2D channel state information determined using the OTFS pilot transmission.
The disclosure also pertains to a method of operating an IOT device which includes initially operating a processor of the IOT device in a low power mode. The operation of the processor is then transitioned to operation in a high power mode at a scheduled time or in response to, for example, a sensor signal. The method further includes transmitting an OTFS waveform during the operation of the processor in the high power mode. The processor then transitions to operation in the low power mode after the OTFS waveform has been transmitted. The process of determining the OTFS transmission waveform may include, for example, receiving, from an IOT manager device, the 2D channel state information and storing it within a memory. Alternatively, at least one OTFS pilot transmission may be received from the IOT manager device and the 2D channel state information determined using the OTFS pilot transmission. The 2D channel state information may be with respect to a delayDoppler channel domain.
The IOT device may be included among a plurality of IOT devices in wireless communication with the IOT manager device. In one implementation the plurality of IOT devices are configured to transmit OTFS waveforms generated using OTFS data symbols occupying locations within an OTFS data frame uniquely corresponding to ones of the plurality of IOT devices.
The disclosure is also directed to a method of operating an IOT device which includes selecting an OTFS data symbol in response to an input signal. An OTFS waveform is generated using the OTFS data symbol and twodimensional (2D) channel state information. The OTFS data symbol may be included within an OTFS data frame wherein the IOT device is included among a plurality of IOT devices uniquely associated with locations within the OTFS data frame. The method further includes transmitting the OTFS waveform.
In yet another aspect the disclosure pertains to a method for operating an IOT manager device in wireless communication with a plurality of IOT devices. The method includes receiving, from the plurality of IOT devices, a corresponding plurality of sets of OTFS pilot waveforms. A plurality of twodimensional (2D) channel states are then determined based upon the plurality of sets of OTFS pilot waveforms. Information relating to the plurality of 2D channel states may then be transmitted to corresponding ones of the plurality of IOT devices. Each of the 2D channel states may be with respect to a delayDoppler channel domain.
Aspects of the disclosure are also directed to a method for operating an IOT manager device in wireless communication with a plurality of IOT devices. The method includes transmitting OTFS pilot waveforms generated using OTFS pilot symbols occupying locations within an OTFS data frame reserved for the pilot symbols. A plurality of OTFS data transmissions are received from the plurality of IOT devices wherein the plurality of OTFS data transmissions are generated using a corresponding plurality of OTFS data symbols associated with a plurality of locations in the OTFS data frame respectively corresponding to the plurality of IOT devices. In one implementation the plurality of locations are different from the locations within the OTFS data frame reserved for the pilot symbols.
In a further aspect the disclosure pertains to an IOT device including a wireless transceiver, a processor capable of operating in a low power mode and in a high power mode, and a memory including program code executable by a processor. The program code may be configured to effectuate any of the abovedescribed methods pertinent to operating an IOT device.
The disclosure is further directed to an IOT manager device in wireless communication with a plurality of IOT devices. The IOT manager device includes a wireless transceiver, a processor and a memory including program code executable by a processor. The program code may be configured to effectuate any of the abovedescribed methods pertinent to operating an IOT manager device.
For a better understanding of the nature and objects of various embodiments of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings, wherein:
Disclosed herein are systems, methods and devices for establishing and maintaining communication between IOT devices and one or more IOT managers using OTFS communication techniques. In order to facilitate understanding of these various systems, methods and devices, an explanation of an exemplary OTFS communication system and techniques is provided prior to discussing the integration of such techniques within IOT devices and systems.
As is discussed below, OTFS is a modulation technique that involves transmitting each information symbol by modulating a twodimensional (2D) basis function on the timefrequency plane. In exemplary embodiments the modulation basis function set is specifically derived to best represent the dynamics of the time varying multipath channel. In this way OTFS transforms the timevarying multipath channel into a time invariant delayDoppler two dimensional convolution channel. This effectively eliminates the difficulties in tracking timevarying fading in, for example, communications involving high speed vehicles.
OTFS increases the coherence time of the channel by orders of magnitude. It simplifies signaling over the channel using well studied AWGN codes over the average channel SNR. More importantly, it enables linear scaling of throughput with the number of antennas in moving vehicle applications due to the inherently accurate and efficient estimation of channel state information (CSI). In addition, since the delayDoppler channel representation is very compact, OTFS enables massive MIMO and beamforming with CSI at the transmitter for four, eight, and more antennas in moving vehicle applications. The CSI information needed in OTFS is a fraction of what is needed to track a time varying channel.
In deep building penetration use cases, one QAM symbol may be spread over multiple time and/or frequency points. This is a key technique to increase processing gain and in building penetration capabilities for IoT deployment and PSTN replacement applications. Spreading in the OTFS domain allows spreading over wider bandwidth and time durations while maintaining a stationary channel that does not need to be tracked over time.
These benefits of OTFS will become apparent once the basic concepts behind OTFS are understood. There is a rich mathematical foundation of OTFS that leads to several variations; for example it can be combined with OFDM or with multicarrier filter banks. Prior to proceeding to a detailed discussion of OTFS, various drawbacks of communication systems predicated on onedimensional channel models are first described.
Turning now to
Unfortunately, use of a onedimensional channel model presents a number of fundamental problems. First, the onedimensional channel models employed in existing communication systems are nonstationary; that is, the symboldistorting influence of the communication channel changes from symbol to symbol. In addition, when a channel is modeled in only one dimension it is likely and possible that certain received symbols will be significantly lower in energy than others due to “channel fading”. Finally, onedimensional channel state information (CSI) appears random and much of it is estimated by interpolating between channel measurements taken at specific points, thus rendering the information inherently inaccurate. These problems are only exacerbated in multiantenna (MIMO) communication systems. As is discussed below, embodiments of the OTFS method described herein can be used to substantially overcome the fundamental problems arising from use of a onedimensional channel model.
The multipath fading channel is commonly modeled onedimensionally in the baseband as a convolution channel with a time varying impulse response
r(t)=∫(τ,t)s(t−τ)dτ (1)
where s(t) and r(t) represent the complex baseband channel input and output respectively and where (τ, t) is the complex baseband time varying channel response.
This representation, while general, does not give us insight into the behavior and variations of the time varying impulse response. A more useful and insightful model, which is also commonly used for Doppler multipath doubly fading channels is
r(t)=∫∫h(τ,ν)e^{j2πν(t−τ)}s(t−τ)dνdτ (2)
In this representation, the received signal is a superposition of reflected copies of the transmitted signal, where each copy is delayed by the path delay τ, frequency shifted by the Doppler shift ν and weighted by the timeindependent delayDoppler impulse response h(τ, ν) for that τ and ν. In addition to the intuitive nature of this representation, Eq. (2) maintains the generality of Eq. (1). In other words it can represent complex Doppler trajectories, like accelerating vehicles, reflectors etc. This can be seen if we express the time varying impulse response as a Fourier expansion with respect to the time variable t
(τ,t)=∫h(τ,ν)e^{j2πνt}dt (3)
Substituting (3) in (1) we obtain Eq. (2) after some manipulation. As an example,
An important feature revealed by these two figures is how compact the (τ, ν) representation is compared to the (τ, t) representation. This has important implications for channel estimation, equalization and tracking as will be discussed later.
Notice that while h(τ, ν) is, in fact, timeindependent, the operation on s(t) is still time varying, as can be seen by the effect of the explicit complex exponential function of time in Eq. (2). The technical efforts in this paper are focused on developing a modulation scheme based on appropriate choice of orthogonal basis functions that render the effects of this channel to become truly timeindependent in the domain defined by those basis functions. The proposed scheme has the following high level outline.
Let us consider a set of orthonormal basis functions φ_{τ,ν}(t) indexed by τ, ν which are orthogonal to translation and modulation, i.e.,
φ_{τ,ν}(t−τ_{0})=φ_{τ+τ}_{0}_{ν}(t)
e^{j2πν}^{0}^{t}φ_{τ,ν}(t)=φ_{τ,ν−ν}_{0}(t) (4)
and let us consider the transmitted signal as a superposition of these basis functions
s(t)=∫∫x(τ,ν)φ_{τ,ν}(t)dτdν (5)
where the weights x(τ, ν) represent the information bearing signal to be transmitted. After the transmitted signal of (5) goes through the time varying channel of Eq. (2) we obtain a superposition of delayed and modulated versions of the basis functions, which due to (4) results in
where * denotes two dimensional convolution. Eq. (6) can be thought of as a generalization of the convolution relationship for linear time invariant systems, using one dimensional exponentials as basis functions. Notice that the term in brackets can be recovered at the receiver by matched filtering against each basis function φ_{τ,ν}(t). In this way a two dimensional channel relationship is established in the (τ, ν) domain
y(τ,ν)=h(τ,ν)*x(τ,ν) (7)
where y(τ, ν) is the receiver two dimensional matched filter output. Notice also, that in this domain the channel is described by a time invariant twodimensional convolution.
A final different interpretation of the wireless channel will also be useful in what follows. Let us consider s(t) and r(t) as elements of the Hilbert space of square integrable functions . Then Eq. (2) can be interpreted as a linear operator on acting on the input s(t), parameterized by the impulse response h(τ, ν), and producing the output r(t)Cooley SpecZZMPTAG
Notice that although the operator is linear, it is not timeinvariant. If there is no Doppler, i.e., if h(ν, τ)=h(0, τ)δ(ν), then Eq. (2) reduces to a time invariant convolution. Also notice that while for time invariant systems the impulse response is parameterized by one dimension, in the time varying case we have a two dimensional impulse response. While in the time invariant case the convolution operator produces a superposition of delays of the input s(t), (hence the parameterization is along the one dimensional delay axis) in the time varying case we have a superposition of delayandmodulate operations as seen in Eq. (2) (hence the parameterization is along the two dimensional delay and Doppler axes). This is a major difference which makes the time varying representation noncommutative (in contrast to the convolution operation which is commutative), and complicates the treatment of time varying systems.
The important point of Eq. (8) is that the operator Π_{h}(•) can be compactly parameterized by a two dimensional function h(τ, ν), providing an efficient, timeindependent description of the channel. Typical channel delay spreads and Doppler spreads are a very small fraction of the symbol duration and subcarrier spacing of multicarrier systems.
In the mathematics literature, the representation of time varying systems of (2) and (8) is called the Heisenberg representation [4]. It can actually be shown that every linear operator (8) can be parameterized by some impulse response as in (2).
The time variation of the channel introduces significant difficulties in wireless communications related to channel acquisition, tracking, equalization and transmission of channel state information (CSI) to the transmit side for beamforming and MIMO processing. We herein develop a modulation domain based on a set of orthonormal basis functions over which we can transmit the information symbols, and over which the information symbols experience a static, time invariant, two dimensional channel for the duration of the packet or burst transmission. In that modulation domain, the channel coherence time is increased by orders of magnitude and the issues associated with channel fading in the time or frequency domain in SISO or MIMO systems are significantly reduced.
The components of the OTFS transceiver may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof.
Referring now to
We start our description with the transform which relates the waveform domain to the timefrequency domain.
Our purpose in this section is to construct an appropriate transmit waveform which carries information provided by symbols on a grid in the timefrequency plane. Our intent in developing this modulation scheme is to transform the channel operation to an equivalent operation on the timefrequency domain with two important properties: (i) the channel is orthogonalized on the timefrequency grid; and (ii) the channel time variation is simplified on the timefrequency grid and can be addressed with an additional transform. Fortunately, these goals can be accomplished with a scheme that is very close to wellknown multicarrier modulation techniques, as explained next. We will start with a general framework for multicarrier modulation and then give examples of OFDM and multicarrier filter bank implementations.
Let us consider the following components of a time frequency modulation:
 A lattice or grid on the time frequency plane, that is a sampling of the time axis with sampling period T and the frequency axis with sampling period Δf.
Λ={(nT,mΔf),n,mε} (9)
 A packet burst with total duration NT secs and total bandwidth MΔf Hz
 A set of modulation symbols X[n, m], n=0, . . . , N−1, m=0, . . . , M−1 we wish to transmit over this burst
 A transmit pulse g_{tr}(t) with the property of being orthogonal to translations by T and modulations by Δf (generally required if the receiver uses the same pulse as the transmitter)
Given the above components, the timefrequency modulator is a Heisenberg operator on the lattice Λ, that is, it maps the two dimensional symbols X[n, m] to a transmitted waveform, via a superposition of delayandmodulate operations on the pulse waveform g_{tr}(t)
More formally
where we denote by Π_{X}(•) the “discrete” Heisenberg operator, parameterized by discrete values X[n, m].
Notice the similarity of (12) with the channel equation (8). This is not by coincidence, but rather because we apply a modulation effect that mimics the channel effect, so that the end effect of the cascade of modulation and channel is more tractable at the receiver. It is not uncommon practice, for example, linear modulation (aimed at time invariant channels) is in its simplest form a convolution of the transmit pulse g(t) with a delta train of QAM information symbols sampled at the Baud rate T.
In our case, aimed at the time varying channel, we convolveandmodulate the transmit pulse (c.f the channel Eq. (2)) with a two dimensional delta train which samples the time frequency domain at a certain Baud rate and subcarrier spacing.
The sampling rate in the timefrequency domain is related to the bandwidth and time duration of the pulse g_{tr}(t); namely, its timefrequency localization. In order for the orthogonality condition of (10) to hold for a frequency spacing Δf, the time spacing must be T≧1/Δf. The critical sampling case of T=1/Δf is generally not practical and refers to limiting cases, for example to OFDM systems with cyclic prefix length equal to zero or to filter banks with g_{tr}(t) equal to the ideal Nyquist pulse.
Some examples are now in order:
Let us consider an OFDM system with M subcarriers, symbol length T_{OFDM}, cyclic prefix length T_{CP }and subcarrier spacing 1/T_{OFDM}. If we substitute in Equation (11) symbol duration T=T_{OFDM}+T_{CP}, number of symbols N=1, subcarrier spacing Δf=1/T_{OFDM }and g_{tr}(t) a square window that limits the duration of the subcarriers to the symbol length T
then we obtain the OFDM formula
Technically, the pulse of Eq. (14) is not orthonormal but is orthogonal to the receive filter (where the CP samples are discarded).
Equation (11) reduces to single carrier modulation if we substitute M=1 subcarrier, T equal to the Baud period and g_{tr}(t) equal to a square root raised cosine Nyquist pulse.
Equation (11) describes a MCFB if g_{tr}(t) is a square root raised cosine Nyquist pulse with excess bandwith α, T is equal to the Baud period and Δf=(1+α)/T.
Expressing the modulation operation as a Heisenberg transform as in Eq. (12) may be counterintuitive. We usually think of modulation as a transformation of the modulation symbols X[m, n] to a transmit waveform s(t). The Heisenberg transform instead, uses X[m, n] as weights/parameters of an operator that produces s(t) when applied to the prototype transmit filter response g_{tr}(t)—c.f. Eq. (12). While counterintuitive, this formulation is useful in pursuing an abstraction of the modulationchanneldemodulation cascade effects in a two dimensional domain where the channel can be described as time invariant.
We next turn our attention to the processing on the receiver side needed to go back from the waveform domain to the timefrequency domain. Since the received signal has undergone the cascade of two Heisenberg transforms (one by the modulation effect and one by the channel effect), it is natural to inquire what the endtoend effect of this cascade is. The answer to this question is given by the following result:
Proposition 1:
Let two Heisenberg transforms as defined by Eqs. (8), (2) be parameterized by impulse responses h_{1}(τ, ν), h_{2}(τ, ν) and be applied in cascade to a waveform g(t)ε. Then
Π_{h}_{2}(Π_{h}_{1}(g(t)))=Π_{h}(g(t)) (16)
where h(τ, ν)=h_{2}(τ, ν)⊙h_{1}(τ, ν) is the “twisted” convolution of h_{1}(τ, ν), h_{2}(τ, ν) defined by the following convolveandmodulate operation
h(τ,ν)=∫∫h_{2}(τ′,ν′)h_{1}(τ−τ′,ν−ν′)e^{j2πν′(ττ′)t}dτ′dν′ (17)
Applying the above result to the cascade of the modulation and channel Heisenberg transforms of (12) and (8), we can show that the received signal is given by the Heisenberg transform
r(t)=Π_{f}(g_{tr}(t))+v(t)=∫∫f(τ,ν)e^{j2πν(tτ)}g_{tr}(t−τ)dνdτ+v(t) (18)
where v(t) is additive noise and f(τ, ν), the impulse response of the combined transform, is given by the twisted convolution of X[n, m] and h(τ, ν)
This result can be considered an extension of the single carrier modulation case, where the received signal through a time invariant channel is given by the convolution of the QAM symbols with a composite pulse, that pulse being the convolution of the transmitter pulse and the channel impulse response.
With this result established we are ready to examine the receiver processing steps.
Receiver Processing and the Wigner Transform
Typical communication system design dictates that the receiver performs a matched filtering operation, taking the inner product of the received waveform with the transmitter pulse, appropriately delayed or otherwise distorted by the channel. In our case, we have used a collection of delayed and modulated transmit pulses, and we need to perform a matched filter on each one of them.
While this approach will yield the sufficient statistics for data detection in the case of an ideal channel, a concern can be raised here for the case of nonideal channel effects. In this case, the sufficient statistics for symbol detection are obtained by matched filtering with the channeldistorted, informationcarrying pulses (assuming that the additive noise is white and Gaussian). In many well designed multicarrier systems however (e.g., OFDM and MCFB), the channel distorted version of each subcarrier signal is only a scalar version of the transmitted signal, allowing for a matched filter design that is independent of the channel and uses the original transmitted subcarrier pulse. We will make these statements more precise shortly and examine the required conditions for this to be true.
In actual embodiments of an OTFS receiver, this matched filtering may be implemented in the digital domain using an FFT or a polyphase transform for OFDM and MCFB respectively. However, for purposes of the present discussion, we will consider a generalization of this matched filtering by taking the inner product <g_{r}(t−τ)e^{j2πν(t−τ)}, r(t)> of the received waveform with the delayed and modulated versions of the receiver pulse for arbitrary time and frequency offset (τ, ν). While likely not a practical implementation, it allows us to view the operations of
Let us define the inner product
A_{g}_{r}_{,r}(τ,ν)=<g_{r}(t−τ)e^{j2πν(t−τ)},r(t)>=∫g*_{r}(t−τ)e^{−j2πν(t−τ)}r(t)dt (20)
The function A_{g}_{r,}_{r}(τ, ν) is known as the crossambiguity function in the radar and math communities and yields the matched filter output if sampled at τ=nT, ν=mΔf (on the lattice Λ), i.e.,
Y[n,m]=A_{g}_{r}_{,r}(τ,ν)_{τ=nT,ν=mΔf} (21)
The ambiguity function is related to the inverse of the Heisenberg transform, namely the Wigner transform.
Π_{A}_{gr,gtr}(y(t))=g_{tr}(t)<g_{r}(t),y(t)>
In words, the coefficients that come out of the matched filter, if used in a Heisenberg representation, will provide the best approximation to the original y(t) in the sense of minimum square error.
The key question here is what the relationship is between the matched filter output Y[n, m] (or more generally Y(τ, ν)) and the transmitter input X[n, m]. We have already established in (18) that the input to the matched filter r(t) can be expressed as a Heisenberg representation with impulse response f(τ, ν) (plus noise). The output of the matched filter then has two contributions
Y(τ,ν)=A_{g}_{r}_{,r}(τ,ν)=A_{g}_{r}_{[Π}_{f}_{(g}_{tr}_{)+v]}(τ,ν)=A_{g}_{r}_{,Π}_{f}_{(g}_{tr}_{)}(τ,ν)+A_{g}_{r}_{,v}(τ,ν) (22)
The last term is the contribution of noise, which we will denote V(τ, ν)=A_{g}_{r,}_{v}(τ, ν). The first term on the right hand side is the matched filter output to the (noiseless) input comprising of a superposition of delayed and modulated versions of the transmit pulse. We next establish that this term can be expressed as the twisted convolution of the two dimensional impulse response f (τ, ν) with the crossambiguity function (or two dimensional cross correlation) of the transmit and receive pulses.
The following theorem summarizes the key result.
Theorem 1:
(Fundamental TimeFrequency Domain Channel Equation).
If the received signal can be expressed as
Π_{f}(g_{tr}(t))=∫∫f(τ,ν)e^{j2πν(tτ)}g_{tr}(t−τ)dνdτ (23)
Then the crossambiguity of that signal with the receive pulse g_{tr}(t) can be expressed as
A_{g}_{r}_{,Π}_{f}_{(g}_{tr}_{)}(τ,ν)=f(τ,ν)⊙A_{g}_{r}_{,g}_{tr}(τ,ν) (24)
Recall from (19) that f(τ, ν)=h(τ, ν)⊙X[n, m], that is, the composite impulse response is itself a twisted convolution of the channel response and the modulation symbols.
Substituting f(τ, ν) from (19) into (22) we obtain the endtoend channel description in the time frequency domain
where V(τ, ν) is the additive noise term. Eq. (25) provides an abstraction of the time varying channel on the timefrequency plane. It states that the matched filter output at any time and frequency point (τ, ν) is given by the delayDoppler impulse response of the channel twistconvolved with the impulse response of the modulation operator twistconvolved with the crossambiguity (or two dimensional cross correlation) function of the transmit and receive pulses.
Evaluating Eq. (25) on the lattice Λ we obtain the matched filter output modulation symbol estimates
{circumflex over (X)}[m,n]=Y[n,m]=Y(τ,ν)_{τ=nT,ν=mΔf} (26)
In order to get more intuition on Equations (25), (26). let us first consider the case of an ideal channel, i.e., h(Tτ, ν)=δ(τ)δ(ν). In this case by direct substitution we get the convolution relationship
In order to simplify Eq. (27) we will use the orthogonality properties of the ambiguity function. Since we use a different transmit and receive pulses we will modify the orthogonality condition on the design of the transmit pulse we stated in (10) to a biorthogonality condition
Under this condition, only one term survives in (27) and we obtain
Y[n,m]=X[n,m]+V[n,m] (29)
where V[n, m] is the additive white noise. Eq. (29) shows that the matched filter output does recover the transmitted symbols (plus noise) under ideal channel conditions. Of more interest of course is the case of nonideal time varying channel effects. We next show that even in this case, the channel orthogonalization is maintained (no intersymbol or intercarrier interference), while the channel complex gain distortion has a closed form expression.
The following theorem summarizes the result as a generalization of (29).
Theorem 2:
(EndtoEnd TimeFrequency Domain Channel Equation):
If h(τ, ν) has finite support bounded by (τ_{max}, ν_{max}) and if A_{g}_{r}_{,g}_{tr}(τ, ν)=0 for τε(nT−τ_{max}, nT+τ_{max}), νε(mΔf−ν_{max}, mΔf+ν_{max}), that is, the ambiguity function biorthogonality property of (28) is true in a neighborhood of each grid point (mΔf, nT) of the lattice Λ at least as large as the support of the channel response h(τ, ν), then the following equation holds
Y[n,m]=H[n,m]X[n,m]
H[n,m]=∫∫h(τ,ν)e^{j2πνnT}e^{−j2π(ν+mΔf)τ}dνdτ (30)
If the ambiguity function is only approximately biorthogonal in the neighborhood of Λ (by continuity), then (30) is only approximately true. Eq. (30) is a fundamental equation that describes the channel behavior in the timefrequency domain. It is the basis for understanding the nature of the channel and its variations along the time and frequency dimensions.
Some observations are now in order on Eq. (30). As mentioned before, there is no interference across X[n, m] in either time n or frequency m.
 The endtoend channel distortion in the modulation domain is a (complex) scalar that needs to be equalized
 If there is no Doppler, i.e. h(τ, ν)=h(τ, 0)δ(ν), then Eq. (30) becomes
which is the wellknown multicarrier result, that each subcarrier symbol is multiplied by the frequency response of the time invariant channel evaluated at the frequency of that subcarrier.
 If there is no multipath, i.e. h(τ, ν)=h(0, ν)δ(τ), then Eq. (30) becomes
Y[n,m]=X[n,m]∫h(ν,0)e^{j2πνnT}dτ (32)
Notice that the fading each subcarrier experiences as a function of time nT has a complicated expression as a weighted superposition of exponentials. This is a major complication in the design of wireless systems with mobility like LTE; it necessitates the transmission of pilots and the continuous tracking of the channel, which becomes more difficult the higher the vehicle speed or Doppler bandwidth is.
We close this section with some examples of this general framework.
In this case the fundamental transmit pulse is given by (14) and the fundamental receive pulse is
i.e., the receiver zeroes out the CP samples and applies a square window to the symbols comprising the OFDM symbol. It is worth noting that in this case, the biorthogonality property holds exactly along the time dimension.
In the case of multicarrier filter banks g_{tr}(t)=g_{r}(t)=g(t). There are several designs for the fundamental pulse g(t). A square root raised cosine pulse provides good localization along the frequency dimension at the expense of less localization along the time dimension. If T is much larger than the support of the channel in the time dimension, then each subchannel sees a flat channel and the biorthogonality property holds approximately.
In summary, in this section we described the one of the two transforms that define OTFS. We explained how the transmitter and receiver apply appropriate operators on the fundamental transmit and receive pulses and orthogonalize the channel according to Eq. (30). We further saw via examples how the choice of the fundamental pulse affect the time and frequency localization of the transmitted modulation symbols and the quality of the channel orthogonalization that is achieved. However, Eq. (30) shows that the channel in this domain, while free of intersymbol interference, suffers from fading across both the time and the frequency dimensions via a complicated superposition of linear phase factors.
In the next section we will start from Eq. (30) and describe the second transform that defines OTFS; we will show how that transform defines an information domain where the channel does not fade in either dimension.
The 2D OTFS Transform
Notice that the timefrequency response H[n, m] in (30) is related to the channel delayDoppler response h(τ, ν) by an expression that resembles a Fourier transform. However, there are two important differences: (i) the transform is two dimensional (along delay and Doppler) and (ii) the exponentials defining the transforms for the two dimensions have opposing signs. Despite these difficulties, Eq. (30) points in the direction of using complex exponentials as basis functions on which to modulate the information symbols; and only transmit on the timefrequency domain the superposition of those modulated complex exponential bases. This is the approach we will pursue in this section. As is discussed below, this approach exploits Fourier transform properties and effectively translates a multiplicative channel in one Fourier domain to a convolution channel in the other Fourier domain.
Given the difficulties of Eq. (30) mentioned above we need to develop a suitable version of Fourier transform and associated sampling theory results. Let us start with the following definitions:
Given a square summable two dimensional sequence X[m, n]ε(Λ) we define
Notice that the above 2D Fourier transform (known as the Symplectic Discrete Fourier Transform in the math community) differs from the more well known Cartesian Fourier transform in that the exponential functions across each of the two dimensions have opposing signs. This is necessary in this case, as it matches the behavior of the channel equation.
Further notice that the resulting x(τ, ν) is periodic with periods (1/Δf, 1/T) This transform defines a new two dimensional plane, which we will call the delayDoppler plane, and which can represent a max delay of 1/Δf and a max Doppler of 1/T. A one dimensional periodic function is also called a function on a circle, while a 2D periodic function is called a function on a torus (or donut). In this case x(τ, ν) is defined on a torus Z with circumferences (dimensions) (1/Δf, 1/T).
The periodicity of x(τ, ν) (or sampling rate of the timefrequency plane) also defines a lattice on the delayDoppler plane, which we will call the reciprocal lattice
The points on the reciprocal lattice have the property of making the exponent in (34), an integer multiple of 2π.
The inverse transform is given by:
where c=TΔf.
We next define a sampled version of x(τ, ν). In particular, we wish to take M samples on the delay dimension (spaced at 1/MΔf) and N samples on the Doppler dimension (spaced at 1/NT). More formally we define a denser version of the reciprocal lattice
So that Λ^{⊥}⊂Λ_{0}^{⊥}. We define discrete periodic functions on this dense lattice with period (1/Δf, 1/T), or equivalently we define functions on a discrete torus with these dimensions
These functions are related via Fourier transform relationships to discrete periodic functions on the lattice Λ, or equivalently, functions on the discrete torus
Z_{0}={(nT,mΔf), m=0, . . . , M−1, n=0, . . . N−1,} (39)
We wish to develop an expression for sampling Eq. (34) on the lattice of (38). First, we start with the following definition.
If X_{p}[k, l] is periodic with period (N, M), then we define
Notice that x_{p}[m, n] is also periodic with period [M, N] or equivalently, it is defined on the discrete torus Z_{0}^{⊥}. Formally, the SFFT(X[n, m]) is a linear transformation from (Z_{0})→(Z_{0}^{⊥}).
Let us now consider generating x_{p}[m, n] as a sampled version of (34), i.e.,
Then we can show that (40) still holds where X_{p}[m, n] is a periodization of X[n, m] with period (N, M)
This is similar to the wellknown result that sampling in one Fourier domain creates aliasing in the other domain.
The inverse discrete (symplectic) Fourier transform is given by
where l=0, . . . , M−1, k=0, . . . , N−1. If the support of X[n, m] is timefrequency limited to Z_{0 }(no aliasing in (41)), then X_{p}[n, m]=X[n, m] for n, mεZ_{0}, and the inverse transform (42) recovers the original signal.
In the math community, the SDFT is called “discrete” because it represents a signal using a discrete set of exponentials, while the SFFT is called “finite” because it represents a signal using a finite set of exponentials.
Arguably the most important property of the symplectic Fourier transform is that it transforms a multiplicative channel effect in one domain to a circular convolution effect in the transformed domain. This is summarized in the following proposition:
Proposition 2:
Let X_{1}[n, m]ε(Z_{0}), X_{2}[n, m]ε(Z_{0}) be periodic 2D sequences. Then
SFFT(X_{1}[n,m]*X_{2}[n,m])=SFFT(X_{1}[n,m])·SFFT(X_{2}[n,m]) (43)
where * denotes two dimensional circular convolution. With this framework established we are ready to define the OTFS modulation.
Discrete OTFS Modulation:
Consider a set of NM QAM information symbols arranged on a 2D grid x[l, k], k=0, . . . , N−1, l=0, . . . , M−1 we wish to transmit. We will consider x[l, k] to be two dimensional periodic with period [N, M]. Further, assume a multicarrier modulation system defined by
 A lattice on the time frequency plane, that is a sampling of the time axis with sampling period T and the frequency axis with sampling period Δf (c.f. Eq. (9)).
 A packet burst with total duration NT secs and total bandwidth MΔf Hz.
 Transmit and receive pulses g_{tr}(t), g_{tr}(t)εL_{2}() satisfying the biorthogonality property of (28)
 A transmit windowing square summable function W_{tr}[n, m]ε(Λ) multiplying the modulation symbols in the timefrequency domain
 A set of modulation symbols X[n, m], n=0, . . . , N−1, m=0, . . . , M−1 related to the information symbols x[k, l] by a set of basis functions b_{k,l}[n, m]
where the basis functions b_{k,l}[n, m] are related to the inverse symplectic Fourier transform (c.f., Eq. (42))
Given the above components, we define the discrete OTFS modulation via the following two steps
X[n,m]=W_{tr}[n,m]SFFT^{−1}(x[k,l])
s(t)=Π_{X}(g_{tr}(t)) (45)
The first equation in (45) describes the OTFS transform, which combines an inverse symplectic transform with a widowing operation. The second equation describes the transmission of the modulation symbols X[n, m] via a Heisenberg transform of g_{tr}(t) parameterized by X[n, m]. More explicit formulas for the modulation steps are given by Equations (42) and (11).
While the expression of the OTFS modulation via the symplectic Fourier transform reveals important properties, it is easier to understand the modulation via Eq. (44), that is, transmitting each information symbol x[k, l] by modulating a 2D basis function b_{k,l}[n, m] on the timefrequency plane.
Discrete OTFS Demodulation:
Let us assume that the transmitted signal s(t) undergoes channel distortion according to (8), (2) yielding r(t) at the receiver. Further, let the receiver employ a receive windowing square summable function W_{r}[n, m]. Then, the demodulation operation consists of the following steps:
 (i) Matched filtering with the receive pulse, or more formally, evaluating the ambiguity function on Λ (Wigner transform) to obtain estimates of the timefrequency modulation symbols
Y[n,m]=A_{g}_{r}_{,y}(τ,ν)_{τ=nT,ν=mΔf} (46)
 (ii) windowing and periodization of Y[n, m]Body TextZZMPTAG

 (iii) and applying the symplectic Fourier transform on the periodic sequence
Y_{p}[n,m]Body TextZZMPTAG
{circumflex over (x)}[l,k]=y[l,k]=SFFT(Y_{p}[n,m]) (48)
The first step of the demodulation operation can be interpreted as a matched filtering operation on the timefrequency domain as we discussed earlier. The second step is there to ensure that the input to the SFFT is a periodic sequence. If the trivial window is used, this step can be skipped. The third step can also be interpreted as a projection of the timefrequency modulation symbols on the orthogonal basis functions
The discrete OTFS modulation defined above points to efficient implementation via discreteandperiodic FFT type processing. However, it does not provide insight into the time and bandwidth resolution of these operations in the context of two dimensional Fourier sampling theory. We next introduce the continuous OTFS modulation and relate the more practical discrete OTFS as a sampled version of the continuous modulation.
Continuous OTFS Modulation:
Consider a two dimensional periodic function x(τ, ν) with period [1/Δf, 1/T] we wish to transmit; the choice of the period may seem arbitrary at this point, but it will become clear after the discussion in the next section. Further, assume a multicarrier modulation system defined by
 A lattice on the time frequency plane, that is a sampling of the time axis with sampling period T and the frequency axis with sampling period Δf (c.f. Eq. (9)).
 Transmit and receive pulses g_{tr}(t), g_{tr}(t)εL_{2}() satisfying the biorthogonality property of (28)
 A transmit windowing function W_{tr}[n, m]ε(Λ) multiplying the modulation symbols in the timefrequency domain
Given the above components, we define the continuous OTFS modulation via the following two steps
X[n,m]=W_{tr}[n,m]SDFT^{−1}(x(τ,ν))
s(t)=Π_{X}(g_{tr}(t)) (50)
The first equation describes the inverse discrete timefrequency symplectic Fourier transform [c.f. Eq. (36)] and the windowing function, while the second equation describes the transmission of the modulation symbols via a Heisenberg transform [c.f. Eq. (11)].
Continuous OTFS Demodulation:
Let us assume that the transmitted signal s(t) undergoes channel distortion according to (8), (2) yielding r(t) at the receiver. Further, let the receiver employ a receive windowing function W_{r}[n, m]ε(Λ). Then, the demodulation operation consists of two steps:
 (i) Evaluating the ambiguity function on Λ (Wigner transform) to obtain estimates of the timefrequency modulation symbols
Y[n,m]=A_{g}_{r}_{,y}(τ,ν)_{τ=nT,ν=mΔf} (51)
 (ii) Windowing and applying the symplectic Fourier transform on the modulation symbols
{circumflex over (x)}(τ,ν)=SDFT(W_{r}[n,m]Y[n,m]) (52)
Notice that in (51), (52) there is no periodization of Y[n, m], since the SDFT is defined on aperiodic square summable sequences. The periodization step needed in discrete OTFS can be understood as follows. Suppose we wish to recover the transmitted information symbols by performing a continuous OTFS demodulation and then sampling on the delayDoppler grid
Since performing a continuous symplectic Fourier transform is not practical we consider whether the same result can be obtained using SFFT. The answer is that SFFT processing will produce exactly the samples we are looking for if the input sequence is first periodized (aliased)—see also (40) (41).
We have now described each of the steps of an exemplary form of OTFS modulation. We have also discussed how the Wigner transform at the receiver inverts the Heisenberg transform at the transmitter [c.f. Eqs. (27), (29)], and similarly for the forward and inverse symplectic Fourier transforms.
In a second operation a SFFT transform translates the timevarying channel in the timefrequency domain to a time invariant one in the delayDoppler domain. The Doppler resolution is 1/T_{f }and the delay resolution is 1/BW. The choice of window can provide a tradeoff between main lobe width (resolution) and side lobe suppression, as in classical spectral analysis.
Channel Equation in the OTFS Domain
A mathematical characterization of the endtoend signal relationship in an OTFS system when a nonideal channel is between the transmitter and receiver will now be provided. Specifically, this section demonstrates how the time varying channel in (2), (8), is transformed to a time invariant convolution channel in the delay Doppler domain.
Proposition 3:
Consider a set of NM QAM information symbols arranged in a 2D periodic sequence x[l, k] with period [M, N]. The sequence x[k, l] undergoes the following transformations:
 It is modulated using the discrete OTFS modulation of Eq. (45).
 It is distorted by the delayDoppler channel of Eqs. (2), (8).
 It is demodulated by the discrete OTFS demodulation of Eqs. (46), (48).
The estimated sequence {circumflex over (x)}[l, k] obtained after demodulation is given by the two dimensional periodic convolution
of the input QAM sequence x[m, n] and a sampled version of the windowed impulse response h_{w}(•),
where h_{w}(τ′, ν′) denotes the circular convolution of the channel response with a windowing function
h_{w}(τ′,ν′)=∫∫e^{−j2πτ}h(τ,ν)w(τ′−τ,ν′−ν)dτdν (55)
To be precise, the window w(τ, ν) is circularly convolved with a slightly modified version of the channel impulse response e^{−j2πντ}h(τ, ν) (by a complex exponential) as can be seen in the equation. The windowing function w(τ, ν) is the symplectic Fourier transform of the timefrequency window W[n, m]
and where W[n, m] is the product of the transmit and receive window.
W[n,m]=W_{tr}[n,m]W_{r}[n,m] (5)
In many cases, the windows in the transmitter and receiver are matched, i.e., W_{tr}[n, m]=W_{0}[n, m] and W_{r}[n, m]=W_{0}*[n, m], hence W[n, m]=W_{0}[n, m]^{2}.
The window effect is to produce a blurred version of the original channel with a resolution that depends on the span of the frequency and time samples available as will be discussed in the next section. If we consider the rectangular (or trivial) window, i.e., W[n, m]=1, n=0, . . . , N−1, m=−M/2, . . . , M/2−1 and zero else, then its SDFT w(τ, ν) in (56) is the two dimensional Dirichlet kernel with bandwidth inversely proportional to N and M.
There are several other uses of the window function. The system can be designed with a window function aimed at randomizing the phases of the transmitted symbols. This randomization may be more important for pilot symbols than data carrying symbols. For example, if neighboring cells use different window functions, the problem of pilot contamination is avoided.
Channel Estimation in the OTFS Domain
There is a variety of different ways a channel estimation scheme could be designed for an OTFS system, and a variety of different implementation options and details. In the section we will only present a high level summary and highlight the key concepts.
A straightforward way to perform channel estimation entails transmitting a sounding OTFS frame containing a discrete delta function in the OTFS domain or equivalently a set of unmodulated carriers in the time frequency domain. From a practical standpoint, the carriers may be modulated with known, say BPSK, symbols which are removed at the receiver as is common in many OFDM systems.
This approach may however be wasteful as the extend of the channel response is only a fraction of the full extend of the OTFS frame (1/T, 1/Δf). For example, in LTE systems 1/T=15 KHz while the maximum Doppler shift f_{d,max }is typically one to two orders of magnitude smaller. Similarly 1/Δf≈67 usec, while maximum delay spread τ_{d,max }is again one to two orders of magnitude less. We therefore can have a much smaller region of the OTFS frame devoted to channel estimation while the rest of the frame carries useful data. More specifically, for a channel with support (±f_{d,max}, ±τ_{max}) we need an OTFS subframe of length (2f_{d,max}/T, 2τ_{max}/Δ_{f}).
In the case of multiuser transmission, each UE can have its own channel estimation subframe positioned in different parts of the OTFS frame. This is akin to multiplexing of multiple users when transmitting Uplink Sounding Reference Signals in LTE. The difference is that OTFS benefits from the virtuous effects of its two dimensional nature. For example, if τ_{max }is 5% of the extend of the delay dimension and f_{d,max }is 5% of the Doppler dimension, the channel estimation subframe need only be 5%×5%=0.25% of the OTFS frame.
Notice that although the channel estimation symbols are limited to a small part of the OTFS frame, they actually sound the whole timefrequency domain via the corresponding basis functions associated with these symbols.
A different approach to channel estimation is to devote pilot symbols on a subgrid in the timefrequency domain. The key question in this approach is the determination of the density of pilots that is sufficient for channel estimation without introducing aliasing. Assume that the pilots occupy the subgrid (n_{0}T, m_{0}Δf) for some integers n_{0}, m_{0}. Recall that for this grid the SDFT will be periodic with period (1/n_{0}T, 1/m_{0}Δf). Then, applying the aliasing results discussed earlier to this grid, we obtain an alias free Nyquist channel support region of (±f_{d,max}, ±τ_{max})=(±½n_{0}T, ±½m_{0}Δf). The density of the pilots can then be determined from this relation given the maximum support of the channel. The pilot subgrid should extend to the whole timefrequency frame, so that the resolution of the channel is not compromised.
OTFSAccess: Multiplexing More than One User
There is a variety of ways to multiplex several uplink or downlink transmissions in one OTFS frame. Here we will briefly review the following multiplexing methods:
Multiplexing in the OTFS delayDoppler domain
Multiplexing in the timefrequency domain
Multiplexing in the code speading domain
Multiplexing in the spatial domain
1. Multiplexing in the DelayDoppler Domain:
This is the most natural multiplexing scheme for downlink transmissions. Different sets of OTFS basis functions, or sets of information symbols or resource blocks are given to different users. Given the orthogonality of the basis functions, the users can be separated at the UE receiver. The UE need only demodulate the portion of the OTFS frame that is assigned to it.
This approach is similar to the allocation of PRBs to different UEs in LTE. One difference is that in OTFS, even a small subframe or resource block in the OTFS domain will be transmitted over the whole timefrequency frame via the basis functions and will experience the average channel response.
In the uplink direction, transmissions from different users experience different channel responses. Hence, the different subframes in the OTFS domain will experience a different convolution channel. This can potentially introduce interuser interference at the edges where two user subframes are adjacent, and would require guard gaps to eliminate it. In order to avoid this overhead, a different multiplexing scheme can be used in the uplink as explained next.
2. Multiplexing in the TimeFrequency Domain:
In this approach, resource blocks or subframes are allocated to different users in the timefrequency domain, burst type of IoT applications.
Notice that in this case, each user employs a slightly different version of the OTFS modulation described. One difference is that each user i performs an SFFT on a subframe (N_{i}, M_{i}), N_{i}≦N, M_{i}≦M. This reduces the resolution of the channel, or in other words reduces the extent of the timefrequency plane in which each user will experience its channel variation. On the other side, this also gives the scheduler the opportunity to schedule users in parts of the timefrequency plane where their channel is best.
If we wish to extract the maximum diversity of the channel and allocate users across the whole timefrequency frame, we can multiplex users via interleaving. In this case, one user occupies a subsampled grid of the timefrequency frame, while another user occupies another subsampled grid adjacent to it.
3. Multiplexing in the TimeFrequency Spreading Code Domain:
Let us assume that we wish to design a random access PHY and MAC layer where users can access the network without having to undergo elaborate RACH and other synchronization procedures. There have been several discussions on the need for such a system to support Internet of Things (IoT) deployments. OTFS can support such a system by employing a spreadspectrum approach. Each user is assigned a different twodimensional window function that is designed as a randomizer. The windows of different users are designed to be nearly orthogonal to each other and nearly orthogonal to time and frequency shifts. Each user then only transmits on one or a few basis functions and uses the window as a means to randomize interference and provide processing gain. This can result in a much simplified system that may be attractive for low cost, short burst type of IoT applications.
4. Multiplexing in the Spatial Domain:
Finally, like other OFDM multicarrier systems, a multiantenna OTFS system can support multiple users transmitting on the same basis functions across the whole timefrequency frame. The users are separated by appropriate transmitter and receiver beamforming operations.
Exemplary Implementations of OTFS Communication Systems
Embodiments of Orthogonal Time Frequency Space (OTFS) modulation are comprised of a cascade of two transformations. The first transformation maps the two dimensional plane where the information symbols reside (and which we call the delayDoppler plane) to the time frequency plane. The second one transforms the time frequency domain to the waveform time domain where actual transmitted signal is constructed. This transform can be thought of as a generalization of multicarrier modulation schemes.
In one aspect a method of OTFS communication involves transmitting at least one frame of data ([D]) from the transmitting device 310 to the receiving device 330 through the communication channel 320, such frame of data comprising a matrix of up to N^{2 }data elements, N being greater than 1. The method comprises convolving, within the OTFS transceiver 3151, the data elements of the data frame so that the value of each data element, when transmitted, is spread over a plurality of wireless waveforms, each waveform having a characteristic frequency, and each waveform carrying the convolved results from a plurality of said data elements from the data frame [D]. Further, during the transmission process, cyclically shifting the frequency of this plurality of wireless waveforms over a plurality of times so that the value of each data element is transmitted as a plurality of cyclically frequency shifted waveforms sent over a plurality of times. At the receiving device 330, the OTFS transceiver 3152 receives and deconvolves these wireless waveforms thereby reconstructing a replica of said at least one frame of data [D]. In the exemplary embodiment the convolution process is such that an arbitrary data element of an arbitrary frame of data ([D]) cannot be guaranteed to be reconstructed with full accuracy until substantially all of these wireless waveforms have been transmitted and received.
Attention is now directed to
xεC
^{N×N }
As is illustrated in
φ_{t}=M(x)=Σx(i,j)φ_{i,j}φ_{i,j}φ_{k,l }
Referring to
φ_{r}y=D(φ_{r})
In one embodiment the OTFS transceiver 4400 may be characterized by a number of variable parameters including, for example, delay resolution (i.e., digital time “tick” or clock increment), Doppler resolution, processing gain factor (block size) and orthonormal basis function. Each of these variable parameters may be represented as follows.
Delay Resolution (Digital Time Tick):
Doppler Resolution:
Processing Gain Factor (Block Size):
N>0
Orthonormal Basis of C^{N×1 }(Spectral Shapes):
U={u_{1}, u_{2}, . . . , u_{N}}
As is illustrated by
where b_{1}, b_{2 }. . . b_{N }are illustrated in
Π(h*x)=Π(h)·Π(x) in particular:
Π(δ_{(τ,O)}*x)=L_{t}·Π(x)
Π(δ_{(ω,107 )}*x)=M_{w}·Π(x)
The Heisenberg representation provides that:
where L_{t }and M_{w }are respectively representative of cyclic time and frequency shifts and may be represented as:
The demodulator 4420 takes a received waveform and transforms it into a TF matrix yεC^{N×N }defined in terms of the Wigner transform and the spectral shapes:
Main property of M and D (Stone von Neumann theorem):
D(h*M(x))=h*x where:
h(τ,w)≈a(τΔT,wΔF)
As illustrated in
y
{circumflex over (x)}
Throughout this description, the use of matrix terminology should be understood as being a concise description of the various operations that will be carried out by either the OTFS transceiver 3151 or the OTFS transceiver 3152. Thus the series of steps used to obtain the coefficients of a particular matrix generally correspond to a set of instructions for the transmitter or receiver electronic circuitry (e.g., the various components of the transmitter 405 and the receiver 455 illustrated in
Thus, when the discussion speaks of multiplying matrices, each data element in the matrix formed by the multiplication can be understood in terms of various multistep operations to be carried out by the transmitter or receiver electronic circuitry (e.g., the transmitter 405 or the receiver 455 as illustrated in
Put into matrix terminology, the OTFS method of convolving the data for a group of symbols over both time, spectrum, and tone or spectralshape can be viewed as transforming the data frame with N^{2 }information elements (symbols) to another new matrix with N^{2 }elements whereby each element in the new transformed matrix, (here called the TFS data matrix) carries information about all elements of the original data frame. In other words the new transformed TFS data matrix will generally carry a weighted contribution from each element of the original data frame matrix [D]. Elements of this TFS data matrix are in turn transmitted and received over successive time intervals.
As previously discussed, in embodiments of the OTFS method the basic unit of convolution and deconvolution (convolution unit) is composed of a matrix of N^{2 }symbols or data elements. Over each time interval, a different waveform may be used for each data element. By contrast, prior art methods generally use the same waveform for each data element. For consistency, the N^{2 }units of data will generally be referred to in this specification as a “data frame”. N may be any value greater than one, and in some embodiments will range from 64 to 256.
One distinction between the OTFS method and conventional modulation schemes may be appreciated by observing that a basic unit of convolution, transmission, reception and deconvolution for a prior art communications protocol may be characterized as a data frame of in symbols or elements “d” operated on spreading codes that send the data for in symbols over one spreading interval time where:
[D_{1×n}]=[d_{1 }d_{2 }. . . d_{n}]
In contrast, embodiments of the OTFS method generally use a different basic unit of convolution, transmission, reception, and deconvolution. In particular, such OTFS embodiments will typically use a larger data frame [D_{N×N}] composed of N^{2 }elements or symbols “d” that, as will be discussed, send the data for these N^{2 }elements over a plurality of spreading interval times (often the plurality is N). The data frame [D_{N×N}] may be expressed as:
In general, references herein to a frame of data may be considered to be a reference to the N×N or N^{2 }matrix such as the one shown above, where at least some elements in the matrix may be zero or null elements. In some embodiments, a frame of data could be nonsquare, or N×M, where N≠M.
As previously discussed, the OTFS method will spread this group of N^{2 }symbols across a communications link over multiple spreading intervals of time (usually at least N spreading intervals or times), where each individual spreading interval of time is composed of at least N time slices. Note that due to potential overhead for synchronization and identification purposes, in some embodiments, extra time slices and/or extra spreading intervals of time may be allocated to provide room for this overhead. Although for clarity of presentation this overhead will generally be ignored, it should be understood that the disclosure is intended to also encompass methods in which such overhead exists.
In exemplary embodiments of the OTFS method the data will thus be transmitted as a complex series of waveforms, usually over wireless radio signals with frequencies usually above 100 MHz, and often above 1 GHz or more. These radio frequencies are then usually received over at least N spreading time intervals, where each spreading time interval is often composed of at least N timeslices. Once received, the original data frame will be deconvolved (i.e. solved for) and the most likely coefficients of the original group of symbols are reconstructed. It should be evident that in order to successfully deconvolve or solve for the original data frame, the receiver will usually have prior knowledge of the time, spectrum, and tone or spectralshape spreading algorithms used by the transmitter.
Attention is now directed to
 1: Construct the matrix product of a first N×N matrix [U1] and [D] (often written as either [U_{1}]*[D] or more simply [U_{1}][D]—here both the “*” and simple close association (e.g. [U_{1}][D]) both are intended to represent matrix multiplication)(stage 342).
 2: Optionally permute [U_{1}][D] by a permutation operation P in order to create a new N×N matrix (stage 344). In general, any invertible permutation operation may be used. P may be an identity operation, or alternatively may be a permutation operation that essentially translates the columns of the original N×N [U_{1}][D] matrix to diagonal elements of a transformed [U_{1}][D]′ matrix.
 3: Upon completing the permutation, optionally multiply the permuted result by a second N×N [U_{2}] matrix (for spectral shaping for example), forming:
 [P([U_{1}][D])][U_{2}] (stage 348).
 4: Transmit this signal, according to methods discussed below (stage 350).
In one embodiment the permutation operation P may optionally be of the form:
b
_{i,j}
=a
_{i,(j−i)mod N }
where [a] is the original matrix (here [U_{1}][D]), and [b] is the new matrix (here P([U_{1}][D]).
For sake of simplicity, the result of this permutation operation may be written as P([U_{1}][D]).
b
_{i,j}
=a
_{i,(j+i)mod N }
Yet another permutation option is illustrated in
Here [U_{1}] and [U_{2}], if being used, can both be unitary N×N matrices, usually chosen to mitigate certain impairments on the (often wireless) communications link, such as wide band noise, narrowband interference, impulse noise, Doppler shift, crosstalk, etc. To do this, rather than simply selecting [U_{1}] and [U_{2}] to be relatively trivial identity matrices [I], or matrices with most of the coefficients simply being placed along the central diagonal of the matrix, [U_{1}] and [U_{2}] will usually be chosen with nonzero coefficients generally throughout the matrix so as to accomplish the desired spreading or convolution of the convolution unit [D] across spectrum and tone or spectralshape space in a relatively efficient and uniform manner. Usually, the matrix coefficients will also be chosen to maintain orthogonality or to provide an ability to distinguish between the different encoding schemes embodied in the different rows of the respective matrices, as well as to minimize autocorrelation effects that can occur when radio signals are subjected to multipath effects.
In reference to the specific case where [U_{1}] may have rows that correspond to pseudorandom sequences, it may be useful to employ a permutation scheme where each successive row in the matrix is a cyclically rotated version of the pseudorandom sequence in the row above it. Thus the entire N×N matrix may consist of successive cyclically rotated versions of a single pseudorandom sequence of length N.
In principle, [U_{1}] and [U_{2}], if both are being used, may be a wide variety of different unitary matrices. For example, [U_{1}] may be a Discrete Fourier Transform (DFT) matrix and [U_{2}] may be a Hadamard matrix. Alternatively [U_{1}] may be a DFT matrix and [U_{2}] may be a chirp matrix. Alternatively [U_{1}] may be a DFT matrix and [U_{2}] may also be a DFT matrix, and so on. Thus although, for purposes of explaining certain aspects of the OTFS method, certain specific examples and embodiments of [U_{1}] and [U_{2}] will be given, these specific examples and embodiments are not intended to be limiting.
Note that a chirp matrix, [V], is commonly defined in signal processing as a matrix where, if Ψ is the chirp rate,
 [V]=diag(Ψ, Ψ^{2}, . . . , Ψ^{n}), Ψ=e^{jψ}, and frequency=e^{jω} where ω is the initial center frequency of the spectrum.
Alternatively, a different chirp matrix may be used, filled with elements of the form:
Where j is the matrix row, k is the matrix column, and N is the size of the matrix.
Other commonly used orthonormal matrices, which may be used for [U_{1}], or [U_{2}] or [U_{3}] (to be discussed), include Discrete Fourier matrices, Polynomial exponent matrices, harmonic oscillatory, matrices, the previously discussed Hadamard matrices, Walsh matrices, Haar matrices, Paley matrices, Williamson matrices, Msequence matrices, Legendre matrices, Jacobi matrices, Householder matrices, Rotation matrices, and Permutation matrices. The inverses of these matrices may also be used.
As will be discussed, in some embodiments, [U_{1}] can be understood as being a timefrequency shifting matrix, and [U_{2}] can be understood as being a spectral shaping matrix. In order to preserve readability, [U_{1}] will thus often be referred to as a first timefrequency shifting matrix, and [U_{2}] will thus often be referred to as a second spectral shaping matrix. However use of this nomenclature is also not intended to be limiting. In embodiments in which the optional permutation or multiplication by a second matrix [U2] is not performed, the [U1] matrix facilitates time shifting by providing a framework through which the elements of the resulting transformed data matrix to be transmitted at different times (e.g., on a column by column basis or any other ordered basis).
Turning to some more specific embodiments, in some embodiments, [U_{1}] may have rows that correspond to Legendre symbols, or spreading sequences, where each successive row in the matrix may be a cyclically shifted version of the Legendre symbols in the row above it. These Legendre symbols will occasionally also be referred to in the alternative as base vectors, and occasionally as spectrumspreading codes.
In some embodiments, [U_{2}] may chosen to be a Discrete Fourier transform (DFT) matrix or an Inverse Discrete Fourier Transform matrix (IDFT). This DFT and IDFT matrix can be used to take a sequence of real or complex numbers, such as the N×N (P[U_{1}][D]) matrix, and further modulate P([U_{1}][D]) into a series of spectral shapes suitable for wireless transmission.
The individual rows for the DFT and IDFT matrix [U_{2}] will occasionally be referred in the alternative as Fourier Vectors. In general, the Fourier vectors may create complex sinusoidal waveforms (tone or spectralshapes) of the type:
where, for an N×N DFT matrix, X is the coefficient of the Fourier vector in row k, column N of the DFT matrix, and j is the column number. The products of this Fourier vector can be considered to be tones or spectralshapes.
Although certain specific [U_{1}] and [U_{2}] can be used to transmit any given data frame [D], when multiple data frames [D] are being transmitted simultaneously, the specific [U_{1}] and [U_{2}] chosen may vary between data frames [D], and indeed may be dynamically optimized to avoid certain communications link impairments over the course of transmitting many data frames [D] over a communications session.
This process of convolution and modulation will normally be done by an electronic device, such as a microprocessor equipped, digital signal processor equipped, or other electronic circuit that controls the convolution and modulation parts of the wireless radio transmitter. Similarly the process of receiving and demodulation will also generally rely upon a microprocessor equipped, digital signal processor equipped, or other electronic circuit that controls the demodulation, accumulation, and deconvolution parts of the wireless radio receiver.
Thus again using matrix multiplication, and again remembering that these are all N×N matrices, [P([U_{1}][D])][U_{2}], where [U_{2}] is optional, represents the TFS data matrix that the transmitter will distribute over a plurality of time spreading intervals, time slices, frequencies, and spectral shapes. Note again that as a result of the various matrix operation and optional permutation steps, a single element or symbol from the original N×N data matrix [D] after modulation and transmission, will be distributed throughout the different time spreading intervals, time slices, frequencies, and spectral shapes, and then reassembled by the receiver and deconvolved back to the original single data element of symbol.
In the example of
The matrix [U_{2}] 604 may be a DFT or IDFT matrix and is often designed to spectrally shape the signals. For example, in some embodiments the matrix [U_{2}] 604 may contain the coefficients to direct the transmitter circuits of the OTFS modulator 430 to transform the signals over time in a OFDM manner, such as by quadratureamplitude modulation (QAM) or phaseshift keying, or other scheme.
Usually the matrix [D] 601 will be matrix multiplied by the matrix [U_{1}] 602 by the digital encoder 665 at stage 610, and the matrix product of this operation [U_{1}][D] then optionally permuted by the digital encoder 665 forming P([U_{1}][D]) (stage 611). In embodiments in which a spectral shaping matrix is utilized, the digital encoder 665 multiplies matrix [U_{1}][D] by matrix [U_{2}] 604 forming an N×N TFS data matrix, which may also be referred to herein as an OFTS transmission matrix (stage 614).
The various elements of the TFS matrix are then selected by the OTFS analog modulator 670, usually a column of N elements at a time, on a single element at a time basis (stage 616) The selected elements are then used to generate a modulated signal that is transmitted via an antenna 680 (stage 618). More specifically, in one embodiment the particular real and imaginary components of each individual TFS matrix element are used to control a time variant radio signal 620 during each time slice. Thus one Nelement column of the TFS matrix will usually be sent during each timespreading interval 608, with each element from this column being sent in one of N time slices 612 of the timespreading interval 608. Neglecting overhead effects, generally a complete N×N TFS matrix can be transmitted over N single time spreading intervals 622.
Attention is now directed to
Attention is now directed to
Thus every successive time slice, one element from the TFS matrix 2108 will be used to control the modulator 2104. In one embodiment of the OTFS method, the modulator 2104 includes modules 2132 and 2134 for separating the element into its real and imaginary components, modules 2142 and 2144 for chopping the resultant real and imaginary components, and filtering modules 2152 and 2154 for subsequently performing filtering operations. The filtered results are then used to control the operation of sin and cosine generators 2162 and 2164, the outputs of which are upconverted using a RF carrier in order to produce an analog radio waveform 2120. This waveform then travels to the receiver where it is demodulated and deconvolved as will be described below with reference to
In an alternative embodiment, diagonals of the TFS data matrix may be transmitted over a series of single timespreading intervals, one diagonal per single timespreading interval, so that N diagonals of the final N×N transmission matrix are transmitted over N time intervals. In other embodiments the order in which individual elements of the TFS transmission matrix [[U_{1}][D]][U_{2}] are transmitted across the communications link is determined by a transmit matrix or transmit vector.
In some embodiments, there may be some overhead to this basic model. Thus, for example, with some time padding (additional time slices or additional time spreading intervals), checksums or other verification/handshaking data, which could be transmitted in a nonconvolved manner, could be sent back by the receiver to the transmitter on a per timespreading interval, per N time spreading intervals, or even on a per time slice interval in order to request retransmission of certain parts of the TFS data matrix as needed.
Attention is now directed to
As previously discussed, in some embodiments, the first N×N time spreading matrix [U_{1}] may be constructed out of N rows of a cyclically shifted Legendre symbols or pseudorandom number of length N. That is, the entire N×N spreading matrix is filled with all of the various cyclic permutations of the same Legendre symbols. In some embodiments, this version of the [U_{1}] matrix can be used for spectrum spreading purposes and may, for example, instruct the transmitter to rapidly modulate the elements of any matrix that it affects rapidly over time, that is, with a chip rate that is much faster than the information signal bit rate of the elements of the matrix that the Legendre symbols are operating on.
In some embodiments, the second N×N spectral shaping matrix [U_{2}] can be a Discrete Fourier Transform (DFT) or an Inverse Discrete Fourier Transform (IDFT) matrix. These DFT and IDFT matrices can instruct the transmitter to spectrally shift the elements of any matrix that the DFT matrix coefficients act upon. Although many different modulation schemes may be used, in some embodiments, this modulation may be chosen to be an Orthogonal FrequencyDivision Multiplexing (OFDM) type modulation, in which case a modulation scheme such as quadrature amplitude modulation or phaseshift keying may be used, and this in turn may optionally be divided over many closelyspaced orthogonal subcarriers.
Often the actual choice of which coefficients to use for the first N×N timefrequency shifting matrix [U_{1}] and what coefficients to use for the second N×N spectral shaping matrix [U_{2}] may depend on the conditions present in the communications channel 320. If, for example, a communications channel 320 is subjected to a particular type of impairment, such as wide band noise, narrowband interference, impulse noise, Doppler shift, crosstalk and so on, then some first N×N timefrequency shifting matrices and some second N×N spectral shaping matrices will be better able to cope with these impairments. In some embodiments of the OTFS method, the transmitter and receiver will attempt to measure these channel impairments, and may suggest alternate types of first N×N timefrequency shifting matrices [U_{1}] and second N×N spectral shaping matrices to each [U_{2}] in order to minimize the data loss caused by such impairments.
Various modifications of the abovedescribed data transmission process represented by the matrix multiplication [[U_{1}][D]][U_{2}] are also within the scope of the present disclosure and are described below with reference to
Attention is now directed to
Attention is now directed to
 1: Receive ([P([U_{1}][D])][U_{2}])′ (stage 362)
 2: Perform a first left multiplication by the Hermitian matrix of the [U2] matrix [U_{2}^{H}], if one was used for transmission, thus creating P([U_{1}][D]) (stage 364).
 3: Inverse permute this replica by (P([U_{1}][D])P^{−1}, if a permutation was used during transmission, thus creating [U_{1}][D] (stage 368)
 4. Perform a second right multiplication by the Hermitian matrix of the [U_{1}] matrix [U_{I}^{H}], thus recreating [D] (stage 370).
As a consequence of noise and other impairments in the channel, use of information matrices and other noise reduction methods may be employed to compensate for data loss or distortion due to various impairments in the communications link. Indeed, it may be readily appreciated that one advantage of spreading out the original elements of the data frame [D] over a large range of times, frequencies, and spectral shapes as contemplated by embodiments of the OTFS method is that it becomes straightforward to compensate for the loss during transmission of information associated with a few of the many transmission times, frequencies and spectral shapes.
Although various deconvolution methods may used in embodiments of the OTFS method, the use of Hermitian matrices may be particularly suitable since, in general, for any Hermitian matrix [U^{H}] of a unitary matrix [U], the following relationship applies:
[U][U^{H}]=[I] where [I] is the identity matrix.
Communications links are not, of course, capable of transmitting data at an infinite rate. Accordingly, in one embodiment of the OTFS method the first N×N timefrequency shifting matrix ([U_{1}], the second N×N spectral shaping matrix ([U_{2}] (when one is used), and the elements of the data frame, as well as the constraints of the communications link (e.g. available bandwidth, power, amount of time, etc.), are chosen so that in balance (and neglecting overhead), at least N elements of the N×N TFS data matrix can be transmitted over the communications link in one timespreading interval. More specifically (and again neglecting overhead), one element of the N×N TFS data matrix will generally be transmitted during each time slice of each timespreading interval.
Given this rate of communicating data, then typically the entire TFS data matrix may be communicated over N timespreading intervals, and this assumption will generally be used for this discussion. However it should be evident that given other balancing considerations between the first N×N timefrequency shifting matrix, the second N×N spectral shaping matrix, and the elements of the data frame, as well as the constraints of the communications link, the entire TFS data matrix may be communicated in less than N timespreading intervals, or greater than N time spreading intervals as well.
As discussed above, the contents of the TFS data matrix may be transmitted by selecting different elements from the TFS data matrix, and sending them over the communications link, on a one element per time slice basis, over multiple spreading time intervals. Although in principle this process of selecting different elements of the TFS data matrix can be accomplished by a variety of different methods, such as sending successive rows of the TFS data matrix each single time spreading interval, sending successive columns of the TFS data matrix each successive time spreading interval, sending successive diagonals of the TFS data matrix each successive time spreading intervals, and so on, from the standpoint of communications link capacity, minimizing interference, and reducing ambiguity, some schemes are often better than others. Thus, often the [U_{1}] and [U_{2}] matrices, as well as the permutation scheme P, may be chosen to optimize transmission efficiency in response to various impairments in the communications link.
As shown in
 1: For each single timespreading interval, selecting N different elements of the TFS data matrix (often successive columns of the TFS matrix will be chosen)(stage 482).
 2: Over different time slices in the given time spreading interval, selecting one element (a different element each time slice) from the N different elements of the TFS data matrix, modulating this element, and transmitting this element so that each different element occupies its own time slice (stage 484).
 3: Receiving these N different replica elements of the transmitted TFS data matrix over different said time slices in the given time spreading interval (stage 486).
 4: Demodulating these N different elements of the TFS data matrix (stage 488).
 5. Repeating stages 482, 484, 486 and 488 up to a total of N times in order to reassemble a replica of the TFS data matrix at the receiver (stage 490).
This method assumes knowledge by the receiver of the first N×N spreading code matrix [U_{1}], the second N×N spectral shaping matrix [U_{2}], the permutation scheme P, as well as the particular scheme used to select elements from the TFS matrix to transmit over various periods of time. In one embodiment the receiver takes the accumulated TFS data matrix and solves for the original N×N data frame using standard linear algebra methods. It may be appreciated that because each original data symbol from the original data frame [D] has essentially been distributed over the entire TFS data matrix, it may not be possible to reconstruct an arbitrary element or symbol from the data [D] until the complete TFS data matrix is received by the receiver.
Attention is now directed to
As shown in
In order to decode or deconvolve the TFS matrix accumulated during stage 724, the digital OTFS data receiver 780 left multiplies, during a stage 726, the TFS matrix by the Hermitian matrix of the [U_{2}] matrix, i.e., [U_{2}^{H}], established at a stage 704. Next, the digital OTFS data receiver 780 performs, at a stage 728, an inverse permutation (P^{−1}) of the result of this left multiplication. The digital OTFS data receiver 780 then deconvolves the TFS matrix in order to reconstruct a replica 732 of the original data matrix [D] by, in a stage 730, right multiplying the result of stage 728 by the Hermitian of the original N×N timefrequency shifting matrix [U_{1}], i.e., [U_{1}^{H}], established at a stage 702. Because the reconstructed signal will generally have some noise and distortion due to various communications link impairments, various standard noise reduction and statistical averaging techniques, such as information matrices, may be used to assist in the reconstruction process (not shown). Each replicated frame 732 of each original data matrix [D] may be stored within digital data storage 782 (stage 740).
Attention is now directed to
Attention is now directed to
Attention is now directed to
Various modifications of the abovedescribed data reconstruction process are also within the scope of the present disclosure and are described below with reference to
Referring now to
Attention is now directed to
Consistent with one embodiment of the second form of the OTFS method, each element in the input frame of data [D] is multiplied by its corresponding unique waveform, thereby producing a series of N^{2 }weighted unique waveforms. Over one spreading time interval (generally composed of N time slices), all N^{2 }weighted unique waveforms corresponding to each data element in the fame of data [D] are simultaneously combined and transmitted. Further, in this embodiment a different unique basic waveform of length (or duration) of N time slices is used for each consecutive timespreading interval. The set of N unique basic waveforms (i.e., one for each of the N timespreading intervals) form an orthonormal basis. As may be appreciated, embodiments of the second form of the OTFS element contemplate that at least a part of [D] is transmitted within each of the N timespreading intervals.
To receive waveforms modulated and transmitted in accordance with this second form of the OTFS method, the received signal is (over each spreading interval of N time slices), correlated with the set of all N^{2 }waveforms previously assigned to each data element during the transmission process for that specific time spreading interval. Upon performing this correlation, the receiver will produce a unique correlation score for each one of the N^{2 }data elements (the receiver will have or be provided with knowledge of the set of N^{2 }waveforms respectively assigned by the transmitter to the corresponding set of N^{2 }data elements). This process will generally be repeated over all N timespreading intervals. The original data matrix [D] can thus be reconstructed by the receiver by, for each data element, summing the correlation scores over N timespreading intervals. This summation of correlation scores will typically yield the N^{2 }data elements of the frame of data [D].
Turning now to
In
In this alternative scheme or embodiment, the OTFS method can be understood as being a method of transmitting at least one frame of data ([D]) over a communications link, comprising: creating a plurality of timespectrumtone or spectralshape spreading codes operating over a plurality of timespreading intervals, each single timespreading interval being composed of at least one clock intervals; each timespectrumtone or spectralshape spreading code comprising a function of a first timefrequency shifting, a second spectral shaping, and a time spreading code or scanning and transmission scheme.
In an exemplary embodiment, OTFS modulation techniques may be employed to enable data to be sent from multiple users using multiple transmitters (here usually referred to as the multiple transmitter case) to be received by a single receiver. For example, assume multiple users “a”, “b”, “c”, and “d”, each desire to send a frame of data including N elements. Consistent with an embodiment of a multiuser OTFS transmission scheme, a conceptual N×N OTFS transmission matrix shared by multiple users may be created in the manner described below. Specifically, each given user packs their N elements of data into one column of an N×N data frame associated with such user but leaves the other columns empty (coefficients set to zero). The N×N data frame [D_{a}] associated with, and transmitted by, a user “a” may thus be represented as:
Similarly, the N×N data frame [D_{b}] associated with, and transmitted by, a user “b” may thus be represented as
And user “n” sends and N×N data frame [D_{n}]
Thus, transmission of the data frames [D_{a}], [D_{b}] . . . [D_{n}] respectively by the users “a”, “b” . . . “n” results in transmission of the conceptual N×N OTFS transmission matrix, with each of the users being associated with one of the columns of such conceptual transmission matrix. In this way each independent user “a”, “b” . . . “n” transmits its N data elements during its designated slot (i.e., column) within the conceptual N×N OTFS transmission matrix, and otherwise does not transmit information. This enables signals corresponding to the data frames [D_{a}], [D_{b}] . . . [D_{n}] to be received at the receiver as if the conceptual N×N OTFS transmission matrix was representative of a complete data frame sent by only a single transmitter. Once so received at the receiver, the received data frames [D_{a}], [D_{b}] . . . [D_{n}] effectively replicate the conceptual N×N OTFS transmission matrix, which may then be deconvolved in the manner discussed above.
The size of the tiles in
Multiple users that are using different transmitters (or simply multiple transmitters) may communicate over the same communications link using the same protocol. Here, each user or transmitter may, for example, select only a small number of data elements in the N^{2 }sized frame of data to send or receive their respective data. As one example, a user may simply select one column of the frame of data for their purposes, and set the other columns at zero. The user'"'"'s device will then compute TFS data matrices and send and receive them as usual.
Attention is now directed to
Since a portion of the transmitted signal 2702 is a cyclically time shifted waveform, a time deconvolution device 2714 at the receiver, such as the postequalizer 480 of
Thus, the direct signals 2804 impinging upon the receiver 2806 will, in this example, not be frequency shifted. However the Dopplershifted wireless signals 2808 that bounce off of the wireless reflector, here again building 2807, will echo off in a higher frequency shifted form. These higher frequency shifted “echo” reflections 2810 also still have to travel a longer distance to reach receiver 2806, and thus also end up being time delayed as well. As a result, receiver 2806 receives a signal 2812 that is distorted due to the summation of the direct signal 2804 with the time and frequency shifted echo waveforms 2810.
However, as was described above, the OTFS techniques described herein may utilize the transmission of cyclically time shifted and frequency shifted waveforms. Accordingly, a time and frequency deconvolution device 2814 (alternatively a time and frequency adaptive equalizer such as the OTFS demodulator 460 and the OTFS postequalizer 480 of
The net effect of both time and frequency deconvolutions, when applied to transmitters, receivers, and echo sources that potentially exist at different distances and velocities relative to each other, is to allow the receiver to properly interpret the impaired signal. Here, even if the energy received in the primary signal is too low to permit proper interpretation, the energy from the time and/or frequency shifted versions of the signals can be added to the primary signal upon the application of appropriate time and frequency offsets or deconvolution parameters to signal versions, thereby resulting in a less noisy and more reliable signal at the receiver. Additionally, the time and frequency deconvolution parameters can contain useful information as to the relative positions and velocities of the echo location(s) relative to the transmitter and receiver, as well as the various velocities between the transmitter and receiver, and can also help the system characterize some of the signal impairments that occur between the transmitter and receiver.
Thus, in some embodiments the OTFS systems described herein may also provide a method to provide an improved receiver where, due to either one or a combination of echo reflections and frequency offsets, multiple signals associated with such reflections and offsets result in the receiver receiving a time and/or frequency convolved composite signal representative of time and/or frequency shifted versions of the N^{2 }summationsymbolweighted cyclically time shifted and frequency shifted waveforms. Here, the improved receiver will further time and/or frequency deconvolve the time and/or frequency convolved signal to correct for such echo reflections and the resulting time and/or frequency offsets. This will result in both time and frequency deconvolved results (i.e. signals, typically of much higher quality and lower signal to noise ratio), as well as various time and frequency deconvolution parameters that, as will be discussed, are useful for a number of other purposes.
Before going into a more detailed discussion of other applications, however, it is useful to first discuss the various waveforms in more detail.
Embodiments of the OTFS systems and methods described herein generally utilize waveforms produced by distributing a plurality of data symbols into one or more N×N symbol matrices, and using these one or more N×N symbol matrices to control the signal modulation of a transmitter. Here, for each N×N symbol matrix, the transmitter may use each data symbol to weight N waveforms, selected from an N^{2}sized set of all permutations of N cyclically time shifted and N cyclically frequency shifted waveforms determined according to an encoding matrix U, thus producing N symbolweighted cyclically time shifted and cyclically frequency shifted waveforms for each data symbol. This encoding matrix U is chosen to be an N×N unitary matrix that has a corresponding inverse decoding matrix U^{H}. The method will further, for each data symbol in the N×N symbol matrix, sum the N symbolweighted cyclically time shifted and cyclically frequency shifted waveforms, producing N^{2 }summationsymbolweighted cyclically time shifted and cyclically frequency shifted waveforms. The transmitter will transmit these N^{2 }summationsymbolweighted cyclically time shifted and cyclically frequency shifted waveforms, structured as N composite waveforms, over any combination of N time blocks or frequency blocks.
As discussed above, various waveforms can be used to transmit and receive at least one frame of data [D] (composed of a matrix of up to N^{2 }data symbols or elements) over a communications link. Here each data symbol may be assigned a unique waveform (designated a corresponding waveform) derived from a basic waveform.
For example, the data symbols of the data matrix [D] may be spread over a range of cyclically varying time and frequency shifts by assigning each data symbol to a unique waveform (corresponding waveform) which is derived from a basic waveform of length N time slices (in embodiments described herein the set of N time slices correspond to the time required to transmit this waveform, also referred to as a time block), with a data symbol specific combination of a time and frequency cyclic shift of this basic waveform.
In one embodiment each symbol in the frame of data [D] is multiplied by its corresponding waveform, producing a series of N^{2 }weighted unique waveforms. Over one spreading time interval (or time block interval), all N^{2 }weighted unique waveforms corresponding to each data symbol in the fame of data [D] are simultaneously combined and transmitted. Further, a different unique basic waveform of length (or duration) of one time block (N time slices) may be used for each consecutive timespreading interval (consecutive time block). Thus a different unique basic waveform corresponding to one time block may be used for each consecutive timespreading interval, and this set of N unique waveforms generally forms an orthonormal basis. Essentially, each symbol of [D] is transmitted (in part) again and again either over all N time blocks, or alternatively over some combination of time blocks and frequency blocks (e.g. assigned frequency ranges).
To receive data over each block of time, the received signal is correlated with a corresponding set of all N^{2 }waveforms previously assigned to each data symbol by the transmitter for that specific time block. Upon performing this correlation, the receiver may produce a unique correlation score for each one of the N^{2 }data symbols. This process will be repeated over some combination of time blocks and frequency blocks until all N blocks are received. The original data matrix [D] can thus be reconstructed by the receiver by summing, for each data symbol, the correlation scores over N time blocks or frequency blocks, and this summation of the correlation scores will reproduce the N^{2 }data symbols of the frame of data [D].
Note that in some embodiments, some of these N time blocks may be transmitted nonconsecutively, or alternatively some of these N time blocks may be frequency shifted to an entirely different frequency range, and transmitted in parallel with other time blocks from the original set of N time blocks in order to speed up transmission time. This is discussed later and in more detail in reference to
In order to enable focus to be directed to the underlying cyclically time shifted and cyclically shifted waveforms, detailed aspects of one embodiment of the OTFS methods described above may be somewhat generalized and also discussed in simplified form. For example, the operation of selecting from an N^{2 }set of all permutations of N cyclically time shifted and N cyclically frequency shifted waveforms may correspond, at least in part, to an optional permutation operation P as well as to the other steps discussed above. Additionally, the N^{2 }set of all permutations of N cyclically time shifted and N cyclically frequency shifted waveforms may be understood, for example, to be at least partially described by a Discrete Fourier transform (DFT) matrix or an Inverse Discrete Fourier Transform matrix (IDFT). This DFT and IDFT matrix can be used by the transmitter, for example, to take a sequence of real or complex numbers and modulate them into a series of different waveforms.
Considering now a particular example, individual rows of a DFT matrix (e.g., the DFT matrix of
where, for an N×N DFT matrix [X}, X_{j}^{k }is the coefficient of the Fourier vector in row k column j of the DFT matrix, and N is the number of columns. The products of this Fourier vector may be considered to represent one example of a manner in which the various time shifted and frequency shifted waveforms suitable for use in the OTFS system may be generated.
For example and as mentioned previously,
Although previously discussed,
As described above with reference to
Referring now to
As yet another alternative, some of the various waveform time blocks may be frequency transposed to entirely different frequency bands or ranges 2912, 2914, 2916. This can speed up transmission time, because now multiple waveform time blocks can now be transmitted at the same time as different frequency blocks. As shown in time/frequency offset tiles 2918 and 2920, such multiple frequency band transmissions can also be done on a contiguous, sparse contiguous, contiguous interleaved, or sparse contiguous interleaved manner. Here 2922 and 2928 represent one time block at a first frequency range 2912, and 2924 and 2930 represent the next time block at the frequency range 2912. Here the various frequency ranges 2912, 2914, and 2916 can be formed, as will be described shortly, by modulating the signal according to different frequency carrier waves. Thus, for example, frequency range or band 2912 might be transmitted by modulating a 1 GHz frequency carrier wave, frequency range or band 2914 might be transmitted by modulating a 1.3 GHz frequency carrier wave, and band 2915 might be transmitted by modulating a 1.6 GHz frequency carrier wave, and so on.
Stated differently, the N composite waveforms, themselves derived from the previously discussed N^{2 }summationsymbolweighted cyclically time shifted and cyclically frequency shifted waveforms, may be transmitted over at least N time blocks. These N time blocks may be either transmitted consecutively in time (e.g. 2902, 2904) or alternatively transmitted timeinterleaved with the N time blocks from a second and different N×N symbol matrix.
For both
Attention is now again directed to
In some embodiments the transmitter 2100 may further implement a preequalization operation, typically performed by the preequalizer 410 of
In
In one embodiment the equalizer 3102 produces a set of equalization parameters 3108 during the process of equalizing the distorted waveform. For example, in the simple case where the original waveform was distorted by only a single echo reflection offset by time t_{offset}, and by the time the original waveform and the t_{offset }echo waveform reach the receiver, the resulting distorted signal 3100 may be, for example, about 90% original waveform and 10% t_{offset }echo waveform, then the equalization parameters 3108 can output both the 90% original and 10% echo signal mix, as well as the t_{offset }value. Typically, of course, the actual distorted signal 3100 could consist of a number of various time and frequency offset components, and here again, in addition to cleaning this distortion, the equalizer 3102 can also report the various time offsets, frequency offsets, and percentage mix of the various components of signal 3100 to the transmitter and/or the receiver.
As previously discussed in
In some embodiments, for example in network systems where there may be multiple transmitters and potentially also multiple receivers, it may be useful to transmit the data from the various transmitters using more than one encoding method. Here, for example, a first set of N time blocks may transmit data symbols originating from a first N×N symbol matrix from a first transmitter using a first unitary matrix [U_{1}]. A second set of N time blocks may transmit data symbols originating from a second N×N symbol matrix from a second transmitter using a second unitary matrix [U_{2}]. Depending on the embodiment, [U_{1}] and [U_{2}] may be identical or different. Because the signals originating from the first transmitter may encounter different impairments (e.g. different echo reflections, different frequency shifts), some schemes of cyclically time shifted and cyclically frequency shifted waveforms may operate better than others. Thus, these waveforms, as well as the unitary matrices [U_{1}] and [U_{2}], may be selected based on the characteristics of these particular echo reflections, frequency offsets, and other signal impairments of the system and environment of the first transmitter, the second transmitter and/or the receiver.
As an example, a receiver configured to implement equalization in accordance with
In some cases, before transmitting large amounts of data, or any time as desired, a given transmitter and receiver may choose to more directly test the various echo reflections, frequency shifts, and other impairments of the transmitter and receiver'"'"'s system and environment. This can be done, by, for example having the transmitter send a test signal where the plurality of data symbols are selected to be test symbols known to the receiver (e.g., the receiver may have stored a record of these particular test symbols). Since in this case the receiver will be aware of exactly what sort of signal it should receive in the absence of any impairment, the equalizer 3102 will generally be able to provide even more accurate time and frequency equalization parameters 3108 for use by the receiver relative to the case in which the receiver lacks such awareness. Thus, in this case the equalization parameters provide even more accurate information relating to the characteristics of the echo reflections, frequency offsets, and other signal impairments of the system and environment of the applicable transmitter(s) and the receiver. This more accurate information may be used by the receiver to suggest or command that the applicable transmitter(s) shift to use of communications schemes (e.g., to U matrices) more suitable to the present situation.
In some embodiments, when the transmitter is a wireless transmitter and the receiver is a wireless receiver, and the frequency offsets are caused by Doppler effects, the more accurate determination of the deconvolution parameters, i.e. the characteristics of the echo reflections and frequency offsets, can be used to determine the location and velocity of at least one object in the environment of the transmitter and receiver.
This section includes a description of a number of exemplary OTFS equalization techniques capable of being implemented consistent with the general OTFS equalization approach and apparatus discussed above. However, prior to describing such exemplary techniques, a summary of aspects of transmission and reception of OTFSmodulated signals is given in order to provide an appropriate context for discussion of these OTFS equalization techniques.
Turning now to such a summary of OTFS signal transmission and reception, consider the case in which a microprocessorcontrolled transmitter packages a series of different symbols “d” (e.g. d_{1}, d_{2}, d_{3 }. . . ) for transmission by repackaging or distributing the symbols into various elements of various N×N matrices [D]. In one implementation such distribution may, for example, include assigning d_{1 }to the first row and first column of the [D] matrix (e.g. d_{1}=d_{0,0}), d_{2 }to the first row, second column of the [D] matrix (e.g. d_{2}=d_{0,1}) and so on until all N×N symbols of the [D] matrix are full. Here, if the transmitter runs out of “d” symbols to transmit, the remaining [D] matrix elements can be set to be 0 or other value indicative of a null entry.
The various primary waveforms used as the primary basis for transmitting data, which here will be called “tones” to show that these waveforms have a characteristic sinusoid shape, can be described by an N×N Inverse Discrete Fourier Transform (IDFT) matrix [W], where for each element w in [W],
or alternatively w_{j,k}=e^{ijθ}^{k }or w_{j,k}=[e^{iθ}^{k}]^{j}. Thus the individual data elements d in [D] are transformed and distributed as a combination of various fundamental tones w by a matrix multiplication operation [W]*[D], producing a tone transformed and distributed form of the data matrix, here described by the N×N matrix [A], where [A]=[W]*[D].
To produce N cyclically time shifted and N cyclically frequency shifted waveforms, the tone transformed and distributed data matrix [A] is then itself further permuted by modular arithmetic or “clock” arithmetic, thereby creating an N×N matrix [B], including each element b of [B], b_{i,j}=a_{i,(i+j)modN}. This can alternatively be expressed as [B]=Permute([A])=P(IDFT*[D]). Thus the clock arithmetic controls the pattern of cyclic time and frequency shifts.
The previously described unitary matrix [U] can then be used to operate on [B], producing an N×N transmit matrix [T], where [T]=[U]*[B], thus producing an N^{2 }sized set of all permutations of N cyclically time shifted and N cyclically frequency shifted waveforms determined according to an encoding matrix [U].
Put alternatively, the N×N transmit matrix [T]=[U]*P(IDFT*[D]).
Then, typically on a per column basis, each individual column of N is used to further modulate a frequency carrier wave (e.g. if transmitting in a range of frequencies around 1 GHz, the carrier wave will be set at 1 GHz). In this case each Nelement column of the N×N matrix [T] produces N symbolweighted cyclically time shifted and cyclically frequency shifted waveforms for each data symbol. Effectively then, the transmitter is transmitting the sum of the N symbolweighted cyclically time shifted and cyclically frequency shifted waveforms from one column of [T] at a time as, for example, a composite waveform over a time block of data. Alternatively, the transmitter could instead use a different frequency carrier wave for the different columns of [T], and thus for example transmit one column of [T] over one frequency carrier wave, and simultaneously transmit a different column of [T] over a different frequency carrier wave, thus transmitting more data at the same time, although of course using more bandwidth to do so. This alternative method of using different frequency carrier waves to transmit more than one column of [T] at the same time will be referred to as frequency blocks, where each frequency carrier wave is considered its own frequency block.
Thus, since the N×N matrix [T] has N columns, the transmitter will transmit the N^{2 }summationsymbolweighted cyclically time shifted and cyclically frequency shifted waveforms, structured as N composite waveforms, over any combination of N time blocks or frequency blocks, as previously shown in
On the receiver side, the transmit process is essentially reversed. Here, for example, a microprocessor controlled receiver would of course receive the various columns [T] (e.g., receive the N composite waveforms, also known as the N symbolweighted cyclically time shifted and cyclically frequency shifted waveforms) over various time blocks or frequency blocks as desired for that particular application. In cases in which sufficient bandwidth is available and time is of the essence, the transmitter may transmit the data as multiple frequency blocks over multiple frequency carrier waves. On the other hand, if available bandwidth is more limited, and/or time (latency) is less critical, then the transmit will transmit and the receiver will receive over multiple time blocks instead.
During operation the receiver may effectively tune into the one or more frequency carrier waves, and over the number of time and frequency blocks set for the particular application, eventually receive the data or coefficients from the original N×N transmitted matrix [T] as an N×N receive matrix [R]. In the general case [R] will be similar to [T], but may not be identical due to the existence of various impairments between the transmitter and receiver.
The microprocessor controlled receiver then reverses the transmit process as a series of steps that mimic, in reverse, the original transmission process. The N×N receive matrix [R] is first decoded by inverse decoding matrix [U^{H}], producing an approximate version of the original permutation matrix [B], here called [B^{R}], where [B^{R}]=([U^{H}]*[R]).
The receiver then does an inverse clock operation to back out the data from the cyclically time shifted and cyclically frequency shifted waveforms (or tones) by doing an inverse modular mathematics or inverse clock arithmetic operation on the elements of the N×N [B^{R}] matrix, producing, for each element b^{R }of the N×N [B^{R}] matrix, a_{i,j}^{R}=b_{i,(j−i)modN}^{R}. This produces a decyclically time shifted and decyclically frequency shifted version of the tone transformed and distributed form of the data matrix [A], which may hereinafter be referred to as [A^{R}]. Put alternatively, [A^{R}]=Inverse Permute ([B^{R}]), or [A^{R}]=P^{−1}([U^{H}]*[R]).
The receiver then further extracts at least an approximation of the original data symbols d from the [A^{R}] matrix by analyzing the [A] matrix using an N×N Discrete Fourier Transform matrix DFT of the original Inverse Fourier Transform matrix (IDFT).
Here, for each received symbol d^{R}, the d^{R }are elements of the N×N received data matrix [D^{R}] where [D^{R}]=DFT*A^{R}, or alternatively [D^{R}]=DFT*P^{−1}([U^{H}]*[R]).
Thus the original N^{2 }summationsymbolweighted cyclically time shifted and cyclically frequency shifted waveforms are subsequently received by a receiver which is controlled by the corresponding decoding matrix U^{H }(also represented as [U^{H}]) The processor of the receiver uses this decoding matrix [U^{H}] to reconstruct the various transmitted symbols “d” in the one or more originally transmitted N×N symbol matrices [D] (or at least an approximation of these transmitted symbols).
Turning now to a discussion of various exemplary OTFS equalization techniques, there exist at least several general approaches capable of being used correct for distortions caused by the signal impairment effects of echo reflections and frequency shifts. One approach leverages the fact that the cyclically time shifted and cyclically frequency shifted waveforms or “tones” form a predictable timefrequency pattern. In this scheme a deconvolution device situated at the receiver'"'"'s front end may be straightforwardly configured to recognize these patterns, as well as the echoreflected and frequency shifted versions of these patterns, and perform the appropriate deconvolutions by a pattern recognition process. Alternatively the distortions may be mathematically corrected using software routines, executed by the receiver'"'"'s processor, designed to essentially determine the various echo reflected and frequency shifting effects, and solve for these effects. As a third alternative, once, by either process, the receiver determines the time and frequency equalization parameters of the communication media'"'"'s particular time and frequency distortions, the receiver may transmit a command to the transmitter to instruct the transmitter to essentially precompensate or preencode, e.g., by using a preequalizer such as the preequalizer 410 of
Attention is now directed to
In the embodiment of
Turning now to
Referring to
The timefrequencyspace decision feedback equalizer 4000 advantageously enables the separation of signals within an OTFS communication system in a manner that substantially maximizes utilization of the available bandwidth. Such signal separation is useful in several contexts within an OTFS communication system. These include separation, at a receiver fed by multiple colocated or noncolocated antennas, of signals transmitted by a set of colocated or noncolocated antennas of a transmitter. In addition, the timefrequencyspace decision feedback equalizer 4000 enables the separation, from signal energy received from a remote transmitter, of echoes received by a receive antenna in response to transmissions from a nearby transmit antenna. This echo cancellation may occur even when the transmit and receive signal energy is within the same frequency band, since the twodimensional channelmodeling techniques described herein enable accurate and stationary representations of the both the echo channel and the channel associated with the remote transmitter. Moreover, as is discussed below the signal separation capability of the disclosed timefrequencyspace decision feedback equalizer enables deployment of OTFS transceivers in a mesh configuration in which neighboring OTFS transceivers may engage in full duplex communication in the same frequency band with other such transceivers in a manner transparent to one another.
Again with reference to
At each of the M antenna instances associated with an OTFS receiver, each entry within the twodimensional array of received signal energy being collected will typically include a contribution from each of the N transmit antenna instances involved in transmitting such signal energy. That is, each of the M receive antenna instances collects a mixture of the twodimensional, timefrequency planes of information separately sent by each of the N transmit antenna instances. Thus, the problem to be solved by the equalizer 4000 may be somewhat simplistically characterized as inversion of the N×M “coupling matrix” representative of the various communication channels between the N OTFS transmit antenna instances and the M OTFS receive antenna instances.
In one embodiment each of the N transmit antenna instances sends a pilot signal which may be differentiated from the pilot signals transmitted by the other N−1 antenna instances by its position in the timefrequency plane. These pilot signals enable the OTFS receiver to separately measure each channel and the coupling between each antenna instance. Using this information the receiver essentially initializes the filters present within the equalizer 4000 such that convergence can be achieved more rapidly. In one embodiment an adaptive process is utilized to refine the inverted channel or filter used in separating the received signal energy into different timefrequencyspace planes. Thus, the coupling channel between each transmit and receive antenna instance may be measured, the representation of the measured channel inverted, and that inverted channel representation used to separate the received signal energy into separate and distinct timefrequency planes of information.
System Overview and Device Architectures
Attention is now directed to
The IOT device 10 typically includes at least one processor configured to operate in a plurality of different power modes. When operating in a high power mode the processor is identified by reference numeral 1104PH. In this high power mode the processor 1104PH will generally have more circuits activated and/or will be operating at a higher clock speed (frequency), so as to have a higher processing speed at the cost of more power consumption. When operating in a low power mode the processor is identified by reference numeral 1104PL. In the low power mode the processor 1104PL will typically have fewer circuits activated, and/or be operating at a lower clock speed (frequency), so as to have a lower processing speed at the benefit of lower power consumption. To be clear, the indications of the processor 1104PH and the processor 1104PL in
In some embodiments, at least one processor used in the OTFS IOT device 10 can be a general purpose processor, such as an ARM processor. This IOT device processor(s) can either be a standalone processor, configured as a microcontroller, or alternatively serve embedded in a larger integrated circuit as one or more processor cores, along with other circuit elements such as wireless transceivers, memory, sensors, and the like. In some embodiments it may be useful to supplement more basic processor functionality with additional data and signal processing capability such as various Digital Signal Processor (DSP) circuitry, Field Programmable Gate Array (FPGA) circuitry, various customized instruction sets, and the like.
Although for clarity of presentation only a low power mode and a high power mode are represented in
There also can be additional processor state gradations between low power mode (where the processor is capable of performing calculations, but at a lower rate), and very high power mode (where the processor is running close to its highest speed and highest power consumption).
The IOT device 10 will typically also comprise processor addressable memory (memory) (see
The IOT device 10 will also comprise at least one wireless transceiver 1104t capable of transmitting and receiving OTFS communication signals. The transceiver 1104t may be housed external to the processor 1104PH/PL or, alternatively, may be internal to the same chip or chips used for the processor 1104PH/PL, or some combination thereof (e.g. software controlled radio). The transceiver 1104t should at least be capable of operating when the least one processor is configured as the processor 1104PH, i.e., in a higher power mode. Portions of the transceiver 1104t may optionally also be capable of operating when the at least one processor is configured as the processor 1104PL, i.e., in a lower power mode, but this is not required. In order to simplify explanation, in the examples discussed below it is assumed that at least some portions of the transceiver 1104t (e.g. at least the transmitter) will be in a power down state (e.g., not operating) when the at least one processor is configured as the processor 1104PL, but that when configured as the processor 1104PH. The IOT device 10 will also generally include one or more antennas or antenna connections 1104a, and may also optionally include one or more output devices 30 and sensors 40 as well.
The IOT device 10 will also typically comprise either a battery or other means of obtaining energy (e.g. solar cell, other energy harvesting device, connection to AC electrical power source, and the like). Here, it will generally be assumed that the IOT device 10 only has access to a limited energy source so that in general, it is desirable to limit the energy needs of the IOT device 10 by having the IOT device 10 spend most of the time with its at least one processor 1104PL running in a low power mode. Other IOT device circuits, such as the transceiver 1104t may also be configured to spend most of their time running in a low power mode as well. Thus often the IOT device 10 may spend most of its life in a low power state, perhaps running a few computational tasks (such as OTFS waveform encoding/decoding, sensor monitoring, etc.) in the background, but otherwise saving energy. However occasionally, as will be discussed, the IOT device 10 will transition to a higher power state where it'"'"'s processor is more computationally active, and where the IOT device 10 may be communicating using its transceiver 1104t, and generally expending energy at a substantially higher rate.
In a typical mode of operation, the IOT device 10 will be configured (e.g. often by one or more programs residing in memory 1104m so it can use it'"'"'s at least one clock 20 to configure (e.g. “wake up”) the at least one processor 1104PL from a low power mode, and cause this at least one processor to transition into a higher power mode processor 1104PH. In
When the at least one processor is configured as a high power processor 1104PH operative in a high power mode, the processor 1104PH can also direct the IOT device 10 to use its transceiver 1104t to wirelessly transmit at least one direct OTFS pilot burst(s) 1112a and at least one OTFS data symbol waveform bursts 112a, over the impaired data channel (e.g. over 1100 space with various reflectors 1106 to the at least one IOT manager device 112. The IOT manager device 112 in turn is often connected to or capable of exchanging data with a larger computer network such as the Internet 114 over a connection 118. This connection 118 need not be a wireless connection, but instead can be any medium capable of transmitting and receiving data such as cable, optical fiber, wireless signals, and the like).
In the embodiment of
Unless otherwise specified, assume that the IOT device antenna 1104a is an omnidirectional antenna, and that IOT manager device 112 is also using an omnidirectional antenna. Thus in addition to the direct OTFS pilot bursts and OTFS data symbol waveform bursts 1112a that travel from the IOT device 10 to the IOT manager device 112, other wireless waveforms travel 1114c to the reflector 1106. Wireless waveforms then travel 1114d from the reflector 1106 to the IOT manager device 112.
Turning now to
These multiple path effects are particularly damaging for IOT devices, which, to save energy, are typically transmitted using relatively weak (low energy) wireless waveforms. As discussed extensively both herein and in the related disclosures, however, OTFS methods are designed to cope with such multiple path effects, and thus in effect allow for higher performance IOT devices to be produced.
During operation, the IOT device 10 may transmit OTFS pilot bursts. These OTFS pilot bursts typically comprise a plurality of OTFS pilot symbols Ppt,pf transmitted as OTFS pilot symbol waveform bursts Ppt,pf·Wp(pt, pf), over a plurality of combinations of times pt and frequencies pf. Each pt and pf are unique pilot timefrequency coordinates chosen from a two dimensional pilot OTFS timefrequency grid. These OTFS pilot symbol waveform bursts Ppt,pf·Wp(pt, pf) are typically mutually orthogonal waveform bursts derived from cyclically time and frequency shifted versions of a same OTFS pilot basis waveform Wp.
The IOT manager 112 includes a transceiver (
Direct OTFS pilot (or data) bursts traveling directly 1112a from the at least one transmitter to said at least one receiver; and
Replica OTFS pilot bursts 1114c, 1114d in the form of direct OTFS pilot bursts that have reflected off of the at least one reflector 1106 before reaching the at least one receiver. The impaired data channel thus produces direct OTFS waveform bursts that are further reflector timedelayed and reflector frequencyshifted at the at least one receiver.
When the direct 1112a and replica 1114d waveform bursts reach the at least one receiver of the IOT manager 112, a resulting combination of these transmitter frequency shifted and receiver frequency shifted direct OTFS pilot bursts and any replica OTFS pilot bursts produces channelconvoluted OTFS pilot bursts (and channelconvoluted OTFS data bursts as well). The receiver of the IOT manager 112 can use this bin structure to receive these channelconvoluted OTFS pilot bursts and use at least one IOT manager processor to determine the 2D channel state of the impaired data channel 1100 between the IOT device 10 and the IOT manager 112. This 2D channel state information in turn can be used to correct for various channel imperfections (such as reflector 1106). Importantly, this 2D channel state information can allow the IOT manager device 112 to detect very faint IOT wireless signals at a significantly greater distance than might be possible using prior art methods. In some embodiments, it is contemplated that the IOT device 10 may also transmit data using a plurality of different types of wireless methodologies other than OTFS including, for example, WiFi, Bluetooth, ZigBee, ZWave, and other methods.
More specifically, in
As previously discussed, it is desirable to determine the 2D channel state of an impaired communication channel 1100, and use information obtained from this 2D channel state to help correct for the various distortions that the communications channel imposes on wireless waveforms traversing the data channel. Such distortions include various time delayed echoes of the waveform, as well as various distortions caused by Doppler effects. The 2D channel state can be obtained using either the IOT manager device 112, or by the IOT device 10 itself, by methods discussed hereinafter.
Because the process of obtaining 2D channel state information can be computationally intensive, however, in some embodiments, it may be useful to push the burden of obtaining the 2D channel state information used by the IOT device 10 onto the IOT manager device 112. This is desirable because the IOT manager device 112 may often have access to more power, more powerful processors, as well as possibly more sophisticated transceivers, better antennas, and possibly more extensive apriori information about the distribution of channel impairments (such as reflector 1106 in the vicinity of IOT device 10.
Once the IOT manager device 112 obtains this 2D channel state information, it can in turn communicate this 2D channel state information, as well as possibly also suggestions or commands as to how to decode and precode wireless communications to overcome these 2D channel impairments, to the IOT device 10. This allows the IOT device 10 to have most or all of the advantages of OTFS 2D channel state compensation methods, but at the same time reduces the demands on the possibly limited processor capability of IOT device 10. Importantly, this may not be generally possible when using wireless communication technologies other than OTFS. The stationary nature of the OTFS channel model enables 2D channel state information to remain relevant and useful to the IOT device 10 even in the case in which it is determined slightly earlier in time by the IOT manager 112 and then communicated to the IOT device 10. The rapid time variation of conventional onedimensional, nonstationary channel models generally preclude this approach.
In other embodiments, the IOT device 10 may determine the 2D channel state of the communications channel 1100 using its own processor(s) 1104PH. In this embodiment, also depicted in
Once the IOT device 10 determines the 2D channel state information 82, the at least one IOT device 10 can then use this 2D channel state information 82 and its at least one processor (operating in either low power or higher power mode) to precode at least some its subsequent OTFS wireless transmissions to better compensate for the impaired data channel. For example the IOT device 10 can precode its various direct OTFS pilot bursts and OTFS data symbol waveform bursts that it then transmits to precorrect for channel effects. By the time the various precoded OTFS waveforms reach the IOT manager device 112, the distorting effects of the data channel will largely be removed. In an exemplary embodiment both the OTFS pilot bursts and OTFS data symbol waveform bursts will be distributed within an OTFS timefrequency grid, or “OTFS data frame”, as shown in, for example,
In one embodiment the IOT devices 10a, 10b are time synchronized in the manner described with reference to
In order to ensure that there is no overlap among the OTFS waveforms from different IOT devices 10, in some embodiments the IOT devices 10 may receive information assigning and updating these unique locations from the IOT manager device 112. The overall scheme may be similar to that shown in
In some embodiments, the various unique locations (such as the 1122a “1”, the 1122b “I”, the 1130a “a”, “b”, “c”, and the 1130b “d”, “e”, “f” within the larger OTFS data frames can be separated by at least some unique null locations (here represented by the “0”). Since the various channel impairments tend, due to time delays and frequency shifts, to “push” the transmitted symbols into these null locations, the IOT manager device 112 or IOT device 10 receiving the transmitted symbols may then properly determine that OTFS symbols displaced onto these null regions are done so by the channel impairments. This may simplify the process of determining the 2D channel state of the impaired data channel.
As noted above, due to various power constraints, as well as size and cost constraints, the IOT devices 10 may have rather minimal capability processors and for that matter, rather limited capability transceivers as well. To some extent, the costs, and power utilization of the IOT devices 10 can be optimized by adopting techniques to, for example, precompute the various OTFS waveforms and store them in memory (e.g. 1104m) prior to transmission. If this is done, the transmitter portion of the IOT device transceiver 1104t can consist of as little as a D/A (digital to analog) converter and RF circuitry needed to playback these stored OTFS waveforms as transmitted OTFS waveforms. As a consequence, a slow and relatively primitive IOT processor, running in low power mode, might slowly assemble the OTFS waveforms and store them in memory over a period of hours if needed. Of course faster processors running at high power mode, over much shorter period of times, may also be used.
For that matter, the processor costs and power utilization needed to receive and interpret various OTFS waveforms can also be reduced by, for example, using a receiver portion of the IOT device transceiver that may be little more than RF circuitry and an A/D (analog to digital) converter. This receiver portion may simply digitize the received OTFS waveforms and store them in memory (e. g. 1104m) for later processing by the IOT device processor. Here the IOT device processor 1104PL may be also be running relatively slowly and may be using a relatively simple processor instruction set to do this process over a period of time such as hours. Again faster or more capable processors running at high power mode, over much shorter periods of time, may also be used.
Put alternatively, the IOT device can further use its at least one processor 1104PL, configured to operate in a lower power mode, to precompute one or more mathematical structures 1112tp of various OTFS pilot bursts or OTFS data symbol waveform bursts ahead of time. Usually the precomputed mathematical structure 1112tp can be stored in the IOT devices memory 1104m prior to transmission. In some embodiments the IOT device can use the 2D channel state information 82 to additionally precode the various OTFS mathematical structures and resulting OTFS waveform bursts to precompensate for the properties of the impaired data channel 1100.
Similarly, in some embodiments, IOT devices 10 can be configured to receive OTFS pilot and data symbol waveform bursts from various IOT manager devices 112. Here the IOT devices may operate by sampling and digitizing portions of these received bursts, and storing the digitized portions of these received bursts in the IOT device'"'"'s memory 1104m for either rapid analysis, delayed analysis as desired. When speed of analysis is not a major consideration, then the IOT device can do this analysis using its processor(s) configured to operate in a lower power mode 1104PL, retrieve the digitized received bursts from memory, and analyze the received waveforms. To further correct the received wireless OTFS waveforms from the various channel distortions, the IOT device can optionally also use any previously received or determined 2D channel state information 82. This 2D channel state information contains parameters that the IOT device processor can use to further clean up the various received OTFS wireless waveforms.
The IOT device may then use these received OTFS bursts to perform various tasks, such as using received OTFS pilot bursts to determine information pertaining to a 2D channel state of the impaired wireless data channel 82. Additionally or alternatively, the IOT device can parse or otherwise interpret the received OTFS data symbol bursts for data or commands transmitted by the at least one IOT manager device 112 that are intended that IOT device.
Sensors and Output Devices
In some embodiments, the IOT devices disclosed herein may be configured with one or more sensor devices configured to report on events occurring in the IOT device'"'"'s environment, and/or one or more output devices to enable the IOT device to interact with the IOT device'"'"'s environment. The IOT device'"'"'s processor(s), as well the IOT device software (often stored in the IOT device'"'"'s onboard memory), can control what the IOT device does with any sensor data, as well as how the IOT device configures its output device(s). In some embodiments, the IOT devices may be further configured (often with software) to transmit at least some sensor data to various outside devices (such as the IOT manager device). In some embodiments, the IOT devices may be further configured to receive (from various outside devices such as the IOT manager device) software updates and/or sensor device or output device control information.
Put alternatively, in this embodiment, the IOT device(s) 10 can comprise at least one IOT sensor 10 configured to store IOT sensor data in the IOT device memory 1104m, usually with the aid of the at least one processor (1104PL, 1104PH) running in either low or high power mode as desired. Here the IOT device can also be configured to use it'"'"'s at least one processor to package at least some of the IOT sensor data into at least one IOT sensor data OTFS data symbol waveform burst (here exemplified by 1130e: b′ on the OTFS data grid 1102ge). The OTFS device can then use its at least one wireless transceiver 1104t to transmit this IOT sensor data as at least one IOT sensor OTFS data symbol waveform burst 112e to at least one IOT manager device 112.
Essentially any type of sensor 40 may be managed by an IOT device. As a nonlimiting example, these sensors 40 can include photodetectors, video cameras, infrared sensors, microphones, vibration sensors, motion sensors, accelerometers, stress and strain sensors, MEMS sensors, clinical or vital state sensors, environmental sensors, smoke detectors, carbon monoxide detectors, other chemical detectors, temperature sensors, electrical sensors (e.g. electrical power, voltage, or current sensors, fluid level sensors and the like.
Typically the IOT manager device will be managing a plurality of IOT devices, and indeed may even be managing thousands of different IOT devices at the same time. Thus in this situation, where IOT device may comprises a plurality of IOT devices, all distributed within OTFS wireless range of the IOT manager device 112. Here in some embodiments, each of the individual IOT devices in this plurality of IOT devices may configured by that particular IOT device'"'"'s processor(s) to transmit that individual IOT device'"'"'s respective OTFS pilot bursts and at least one OTFS data symbol waveform bursts at various unique or at least typically reserved locations in one or more OTFS data frames (see
The various IOT devices may be configured so that on a time periodic basis, some or all of the devices transmit sensor data according to a time schedule. For example, IOT devices configured as temperature monitors may report temperature data every 10 minutes on a continual basis. In some embodiments, this schedule may be determined or coordinated by the IOT manager device 112. Indeed the IOT manager may assume executive management functions for the various IOT devices, such as scheduling IOT device data transmission times, managing IOT device software, coordinating which OTFS waveform bursts occupy which timefrequency locations in the OTFS timefrequency grid. Ideally the IOT manager will attempt to schedule these and other functions in advance to optimize the functioning of the various IOT devices, and avoid conflicts.
In the case of the occurrence of unexpected events, such as interrupt driven events caused by unexpected sensor readings or the like, the IOT devices may be configured to “wake up” or otherwise transition to a higher power state ahead of the nominally scheduled time. The IOT device may also need to be configured to use OTFS methods to transmit some sort of previously unscheduled message to the IOT manager device. To cope with this situation, in some embodiments, IOT devices may be further configured to additionally or alternatively perform unscheduled OTFS data transmissions at these typically reserved locations in the OTFS data frame. In other words, these typically “reserved locations” in the OTFS data frame can be timefrequency locations that are reserved for otherwise unscheduled transmissions. These or other “typically reserved locations” can also be used by the systems to allow new IOT devices to initially communicate with IOT manager devices upon initial setup, and before the IOT manager device then assigns an alternate OTFS timefrequency grid set of locations to the newly installed IOT device. An example of such “typically reserved locations” can be seen in
Thus, in the embodiment of
Alternatively, the output device 30 may be configured either according to output data previously stored in IOT memory 1104m, and/or according to direct commands from the IOT device processor (which may be configured in either a low or high energy state). This IOT output data can comprise any of IOT output commands or IOT output control software intended for the IOT device processor (in either low or high power mode), which can then run the IOT output control software, and instruct the processor to in turn configure the status of the IOT output 30. This “status” can be as simple as an “on” or “off” status (in the case where the output is a switch or other binary output device), or it can be very complex (such as if the output is a display screen).
To facilitate response to rapid unscheduled commands in response to unscheduled sensor input, in some embodiments the IOT device may be configured to maintain an otherwise unscheduled high power state for a short time after transmitting the unexpected and unscheduled sensor data. In the event that the IOT device'"'"'s normally scheduled OTFS grid coordinates are unavailable (perhaps because these grid coordinates are also used by one or more alternate IOT devices that are scheduled for different times), the IOT device may also be configured to, at least for a short time, be responsive to IOT manager commands that are sent on a different set of emergency command OTFS grid timefrequency coordinates (similar to the “tr” locations in
Almost any type of output and output device can be controlled by the IOT device 10. Examples of such outputs include outputs that control the supply of power to an external device, controllers, electrical or mechanical or electromechanical actuators (e.g. motors, solenoids, and the like) signal generators, light, image, video and/or audio outputs. An IOT device may, of course have more than one output device 30 and also may have more than one type of output device 30.
IOT Manager Device
Again referring to
In some embodiments, the IOT manager device may be a high performance IOT manager device that further includes circuitry and functionality as discussed in U.S. provisional application 62/129,930, the entire contents of which are incorporated herein by reference. For example, the high performance IOT manager device may optionally implement a more general method of linking other types of local wireless transceivers (e.g. cellular telephones, WiFi devices, etc.) with backbone data access points as well.
Here for example, in addition to any IOT device management functionality, the high performance IOT manager device may optionally operate as a local data access point device comprising at least one processor, at least one OTFS wireless transceiver and at least one nonOTFS wireless transceiver. This at least one OTFS wireless transceiver can be configured to exchange data with at least one nonOTFS wireless transceiver (e.g. a WiFi or cellular telephone transceiver). The high performance IOT manager device can optionally use its at least one OTFS transceiver to establish at least one OTFS wireless link with at least one backbone data access point using at least one OTFS data stream. The high performance IOT manager device can optionally also use its optional nonOTFS wireless transceiver(s) to establish at least one wireless link with at least one other local nonOTFS wireless transceiver. The high performance IOT manger device can then optionally also receive data transmission or reception requirements from either the backbone data access points or local nonOTFS wireless transceivers, and use its at least one processor, at least one OTFS wireless transceiver, and at least one nonOTFS wireless transceiver to direct data to a required recipient according to the data transmission or reception requirements. Regardless of its ability to connect with other, nonOTFS IOT devices, or not, the IOT manager device 112 will typically be configured through software to perform a substantial number of IOT device management and control tasks.
Referring to
With reference to table 800 of
At time point 1, the IOT manager device (112, M1) transmits OTFS signals containing the manager'"'"'s identification number (ID) and a clock synchronization pulse. The clock synchronization pulse can be sent using either the IOT manager'"'"'s device OTFS pilot burst(s) or OTFS data symbol bursts, and the IOT manager'"'"'s identification number (ID) can be sent using the IOT manager'"'"'s OTFS data symbol bursts. In this example the first IOT device1 (10c, D1) is configured to be in high power mode and to listen for the IOT manager device at time point 1. Thus at time point 2 (row 2), after accounting for speed of light transmission delays, the first IOT device D1 receives the ID manager'"'"'s ID and clock. By contrast, in this example the second IOT device (10d, D2) is not yet operating at this time.
At time point 3 (row 3), the first IOT device (10c, D1) interprets the IOT manager'"'"'s OTFS signal, confirms that this is the ID of the master IOT manager, and (if needed) adjusts the time of the first IOT device'"'"'s clock (see
At time point 5, the IOT manager, after checking out the first IOT device'"'"'s ID in the IOT manager'"'"'s database, determines an optimal set of first IOT device settings (e.g. wakeup schedule, OTFS grid coordinates) and then commands the first IOT device to subsequently transmit using a certain set of OTFS grid time frequency coordinates. The IOT manager may optionally transmit the first IOT wakeup schedule as well. The IOT manager may optionally also transmit information pertaining to the 2D channel state of data channel 110 to the first IOT device (see
Meanwhile, on a parallel track, assume that the second IOT device (10d, D2) is switched on for the first time at time point 6 (row 6). Assume that the second IOT device is configured to, after being switched on for the first time, remain in a high power mode for at least some period of time listening for a master IOT manager device to which to slave itself.
At time point 10 (row 10), IOT manager (112, M1) sends out its periodic OTFS manager identification and clock pulse. Due to speed of light delays, this is now received by both the first IOT device (10c, D1) and the second IOT device (10d, D2) at time point 11 (row 11).
At time point 12 (row 12), the first IOT device (10c, D1) sees that it does not need to adjust its clock and recognizing the master IOT manager, and with nothing else to report, simply transmits its identifier (D1ID) back to the IOT manager device, remains in high power mode for any immediate IOT manager commands, and then goes back to low power mode. By contrast, the second IOT device (10d, D2) determines that it needs to adjust its clock (see
At time point 13 (row 13), the IOT manager device receives the ID signals from both the first and second IOT devices. In this example the first IOT device and the second IOT device are configured to transmit partial OTFS data grids as per
Again referring to table 800, while the first IOT device is transmitting on the unique (IOT device specific) OTFS data frame pilot or data coordinates previously assigned by the IOT manager at time point 5 (row 5), the second IOT device has not yet had its unique OTFS data frame pilot or data coordinates assigned by the IOT manager device. Thus the second IOT device initially transmits signals to the IOT manager device using OTFS timefrequency coordinates typically used for unscheduled OTFS data transmissions, such as the
At time point 15 (row 15), the second IOT device receives these commands and follows them. Both the first and second IOT device can then follow their instructions and both go into a lower power “sleep” mode set of synchronized schedules from time points 16 to 18 (rows 16 to 18). Here internal clocks for both IOT devices are both set to wake them both up (transition to high power mode) at time point 19 (row 19), just in time to catch the latest routine IOT manager identification and clock signals transmitted by the IOT manager. This latest routine scheduled transmission is received by both IOT devices at time point 20 (row 20).
In the present example the IOT devices may have accumulated some routine data, possibly from various IOT device sensors 30, and may have both been configured to transmit their respective sets of data (D1data and D2data) back to the IOT manager at the same time. These transmissions may be performed using different unique OTFS data grid timefrequency coordinates, at time point 21 (row 21). In order to save power both IOT devices, following their assigned schedules, may transition back to low power “sleep” mode until time point 25. At time point 25, according to schedule, both of their clocks then automatically wake (transition to high power mode) both IOT devices again at time point 26 (row 26).
At time point 26 (row 26), the IOT manager device 112 then transmits its routine OTFS identification and clock synchronization signals. A time point 27 (row 27), both IOT devices receive these signals. In the present example it is assumed that both IOT devices determine that their respective clocks are set adequately, and thus neither updates their clocks.
At time point 28 (row 28), both IOT devices then transmit their routinely scheduled respective sets of data (D1data and D2 data) to the IOT manager using different unique OTFS data grid timefrequency coordinates (see
In order to illustrate how the present system can cope with nonscheduled exceptions, assume that at time point 29 (row 29) the second IOT device'"'"'s (10d, D2) sensor (40) detects an exception. For example, in the case in which the IOT device'"'"'s sensor 40 was a camera equipped with some sort of facial recognition software, the exception could correspond to the detection of an unfamiliar face 42. However, for purposes of generality, the exception could be any unexpected sensor reading exceeding preprogrammed limits or a threshold defined in the memory of the second IOT device.
In this example, assume that the second IOT device has been configured to not wait for its regular time schedule, but instead to immediately report this exception back to the IOT manager device (112, M1). In this case the IOT device'"'"'s “unique” OTFS grid coordinates may be unavailable to use for such immediate reporting, because the IOT manager 112 may have allocated these “unique” coordinates to other IOT devices 10 on different schedules. In order to avoid this situation, the second IOT device can, at time 30 (row 30), optionally transmit OTFS data signals (corresponding to the second IOT device'"'"'s identification and the exception data) using OTFS grid timefrequency coordinates typically used for unscheduled OTFS data transmissions (such as the
At time point 31 (row 31), the IOT manager device receives the second IOT device'"'"'s identification code and exception data, and then may either automatically, or in response from commands relayed from other computer networks such as the Internet 114, shortly thereafter issue a set of exception handling commands to the second IOT device at time point 32. For purposes of explanation, assume that this exception command is instantly received by the second IOT device and immediately executed by the second IOT device. The exception command may be, for example, a command to change the state of an output of the second IOT device. From time points 33 to 36 (rows 33 to 36), it is assumed that both IOT devices resume the normal cycle of IOT manager device identification and clock synchronization, waking up, receiving IOT manger information, transmitting routine data, and then going back to the earlier described sleep and wake up cycles.
As may be appreciated from
 1: Detect a 2D channel state of the impaired data channel connecting the IOT manager device and the IOT device and use this 2D channel state to determine at least one scheme for compensating or precoding for an effect of the impaired data channel. Transmit this scheme (or the 2D channel state information itself) to this IOT device.
 2: Detect identification codes associated with each of various IOT devices, and assign at least one OTFS data frame pilot or data coordinates to each of these various IOT devices according to these identification codes.
 3a: Receive IOT clock time parameters from at least one IOT device, determine time differences between these IOT clock time parameters and the IOT manager'"'"'s own clock, and transmit time adjustment parameters to the at least one IOT device that can be used by the IOT device to adjust its clock time to reduce these differences.
 3b: Transmit time parameters based on the IOT manager'"'"'s own clock. These will typically be used by the IOT device to adjust the IOT device'"'"'s clock time minimize any differences between the IOT device'"'"'s clock and the IOT manager'"'"'s clock.
 3c: Transmit at least one time schedule for the IOT device to periodically transition or cycle between low power mode and high power mode.
 4: Receive sensor data from those various IOT devices that are equipped with at least one sensor device.
 5: Transmit output data to various IOT devices that are equipped with at least one output device.
 6: Receive or transmit data pertaining to a status of either the IOT manager device or various IOT devices to at least one external computer network (such as the Internet).
Multiple Polarization Modes
In some embodiments at least certain of the IOT devices in communication with the IOT manager may include a wireless transceiver configured to transmit and receive in a plurality of polarization states and a plurality of antennas. During operation the transceiver and antennas cooperate to transmit and receive OTFS wireless waveform bursts over a plurality of polarization states.
Attention is now directed to
As shown in
In the example of
In this example, the impaired data channel 1100 thus can be represented by two somewhat different 2D channel states. One 2D channel state 82h represents the 2D channel state of the impaired data channel as viewed in a first polarization mode such as a horizontal polarization mode. Another 2D channel state 82v represents the 2D channel state of the impaired data channel as viewed in a second polarization mode such as a vertical polarization mode.
It is also observed that the IOT device 10p and the IOT manager 112p can receive and digitize the various polarized OTFS waveforms, here represented by 1112rh and 1112rv, and store them in memory such as 1104m. These OTFS waveforms can then be analyzed or precoded using the 2D channel state information (82h, 82v). This technique is particularly useful for facilitating the ability for the IOT manager to receive faint (low power) OTFS wireless transmissions from the various IOT devices.
MIMO Modes
Before continuing with a discussion of potential MIMO implementations, the concept of a “data stream” will be further described. In a wireless context, if wireless waveforms propagating through a communication channel are separated by different frequencies, or modulated by different (orthogonal to one another) waveforms, then the separated or differently modulated waveforms may be used to carry multiple independent streams of data. As another example, if wireless communications are done between different sets of highly directional transmitter and receiver antennas, with minimal crosstalk between different sets of directional transmitter and receiver antennas, then each transmitter and receiver antenna set can be viewed as forming its own unique communications channel capable of conveying a separate data stream.
Use of OTFS modulation by the IOT manager 112m and IOT devices 10a1, 10a2 further enables simultaneouslytransmitted “streams” of information of information to be differentiated. The number of different “streams” of information a data channel can support can be viewed as varying according to the underlying data channel structure or impairments (e.g. distribution of reflectors) of the data channel. Consider a case where the distribution of reflectors in a data channel is such as to effectively create an isolated conduit between each different transmitting antenna and each different receiving antenna. Such a data channel could give rise to a 2D channel state capable of supporting a large number of different streams of data when MIMO techniques are used in combination with OTFS modulation.
As previously discussed, the 2D channel state information can, in some embodiments, be represented by matrices, and with regards to different streams of information or data, the question of how many different streams can be simultaneously transmitted by a given data channel can be viewed (in linear algebra terms) somewhat in terms of the “rank” of the 2D channel state matrix. This rank is the size of the collection of linearly independent rows (or columns, since column rank is equal to row rank) of the matrix. In some embodiments, this can also be viewed as the number of solutions of the system of linear equations that represent the effect of the impaired data channel on data transmission.
Given this insight, the methods described herein thus teach that using techniques to improve the “rank” of the 2D channel state matrix, such as by using polarization and MIMO, to thus create higher rank 2D channel state matrixes and “richer” data channels. These in turn allow wireless systems to operate at increasingly higher levels of performance (e.g. higher data transmission rates, lower energy per symbol, increased resistance to fading, and the like). In some embodiments, improvement of an order of magnitude and more over prior art methods may be achieved according to these schemes.
Thus in some embodiments, particularly with regards to MIMO techniques, an IOT manager or other entity may determine 2D channel state information and use this in generating OTFS waveforms carrying OTFS data symbols. The wireless transmitter may have T uniquely configured transmitting antennas for transmitting the OTFS waveforms, and the wireless receiver may R uniquely configured receiving antennas. Both T and R will be greater than 1, and R (the number of receiver antennas) may often be greater than or equal to T.
The wireless transmitter may be configured to use its T transmitting antennas to simultaneously transmit, over a same frequency range, at most T streams of stream identifiable direct OTFS data and pilot bursts. Here each stream identifiable direct OTFS data and pilot bursts will preferably have at least their various OTFS pilot symbols P_{s,pt,pf }further chosen to be stream identifiable.
Attention is now directed to
In the example of
For simplicity of explanation and presentation, it is assumed that the MIMO receiver antennas 1304a1 and 1304a2 are receiving and detecting the slightly different travel times for the various transmitter antenna, reflector, and receiver antenna configuration as different delay times on the receiver OTFS timefrequency bin structure. In actuality, the MIMO receiver may often instead detect these differences as differences in the phase angles of the various waveforms, or even as different directions of arrival of the various waveforms, and handle this using receiver bins with additional dimensions, but it is easier to show these differences as time differences to illustrate the concept.
As shown in
In exemplary embodiments the T transmitting antennas and R receiving antennas of a MIMOequipped device supporting OTFS communication should be configured so that the R receiving antennas receive different receiving antenna specific channelconvoluted stream identifiable OTFS data and pilot bursts with detectably different 2D channel states. This can be done by various methods including, for example, ensuring that a sufficiently large separation between the antennas so as to impart an ability for the receiving antennas to sense the directionality of the incoming wireless waveforms. Alternatively, the receiver may be configured so that it further keeps track of the relative phases of the incoming wireless waveforms.
During operation of the MIMOequipped OTFS communication devices of
Transmitter Precoding
In some embodiments, the previously discussed 2D channel state and precoding methods may be further used to shape a spatial directionality of the wireless waveforms transmitted by the T uniquely configured transmitting antennas. Alternatively or additionally, the 2D channel state information and previously discussed 2D channel state assisted deconvolution methods may also be used to shape a spatial directionality of the wireless waveforms received by the R uniquely configured receiving antennas.
As one example, this spatial directionality may be achieved by using the transmitter processor to adjust any of relative phases or angles of the wireless waveforms transmitted by the T uniquely configured transmitting antennas. Alternatively or additionally, the spatial directionality of the wireless waveforms received by the R uniquely configured receiving antennas may be achieved by using the receiver processor to monitor the relative phases or angles of the wireless waveforms received by the R uniquely configured receiving antennas.
In some embodiments, the underlying structure of the 2D data channel (e.g. position of various reflectors, other channel imperfections) may be exploited to enable more than one stream of data at the same time. In this case an OTFS transmitter may transmit the same streamidentifiable OTFS data and pilot bursts through more than one antenna at the same time. In some embodiments a varying phase delay or other adjustment is introduced between the different antennas, thus providing directionality. The same principle may also be employed at the receiver. In such embodiments the transmitter does not configure the stream identifiable direct OTFS data and pilot bursts to be antenna identifiable and or antenna specific. Rather, each different transmitting antenna may transmit at least one stream (e.g. often more than one stream) of wireless OTFS data symbol waveforms along the at least one stream identifiable wireless OTFS pilot symbol waveform. For example, in such embodiments, each transmitting antenna may transmit at least one stream of wireless OTFS data symbol waveforms along with at least one stream identifiable wireless OTFS pilot symbol waveforms according to various transmitting antenna specific phases or power settings. This effectively introduces a spatial directionality to the wireless waveforms transmitted by those transmitting antennas sending the particular stream.
While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. They are not intended to be exhaustive or to limit the claims to the precise forms disclosed. Indeed, many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the described systems and methods and their practical applications, they thereby enable others skilled in the art to best utilize the described systems and methods and various embodiments with various modifications as are suited to the particular use contemplated.
Where methods described above indicate certain events occurring in certain order, the ordering of certain events may be modified. Additionally, certain of the events may be performed concurrently in a parallel process when possible, as well as performed sequentially as described above. Although various modules in the different devices are shown to be located in the processors of the device, they can also be located/stored in the memory of the device (e.g., software modules) and can be accessed and executed by the processors. Accordingly, the specification is intended to embrace all such modifications and variations of the disclosed embodiments that fall within the spirit and scope of the appended claims.
The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the claimed systems and methods. However, it will be apparent to one skilled in the art that specific details are not required in order to practice the systems and methods described herein. Thus, the foregoing descriptions of specific embodiments of the described systems and methods are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the claims to the precise forms disclosed; obviously, many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the described systems and methods and their practical applications, they thereby enable others skilled in the art to best utilize the described systems and methods and various embodiments with various modifications as are suited to the particular use contemplated. It is intended that the following claims and their equivalents define the scope of the systems and methods described herein.
The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
Examples of computer code include, but are not limited to, microcode or microinstructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higherlevel instructions that are executed by a computer using an interpreter. For example, embodiments may be implemented using imperative programming languages (e.g., C, Fortran, etc.), functional programming languages (Haskell, Erlang, etc.), logical programming languages (e.g., Prolog), objectoriented programming languages (e.g., Java, C++, etc.) or other suitable programming languages and/or development tools. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code.
In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other nontransitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded into one or more different computers or other processors to implement various aspects of the present invention as discussed above.
The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computerexecutable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
Computerexecutable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.
Also, data structures may be stored in computerreadable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computerreadable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”
The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a nonlimiting example, a reference to “A and/or B”, when used in conjunction with openended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a nonlimiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
In the claims, as well as in the specification above, all transitional phrases such as “comprising.” “including,” “carrying.” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be openended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semiclosed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.