Hybrid beamforming for data transmission

• US 9,225,404 B2
• Filed: 06/26/2013
• Issued: 12/29/2015
• Est. Priority Date: 03/29/2013
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Intel IP Corporation (Intel Corporation)
• Inventors: Shirani-Mehr, Hooman, Sajadieh, Masoud
• Primary Examiner(s): Vo, Don N
• Application Number:
  US14/125,258
• Publication Number:
  US 20150139292A1
• Time in Patent Office: 916 Days
• Field of Search: 375/267
• US Class Current: 1/1
• CPC Class Codes: H04B 1/56 with provision for simultaneous communication in two directions
• CPC Class Codes: H04B 7/0417 Feedback systems
• CPC Class Codes: H04B 7/0452 Multi-user MIMO systems
• CPC Class Codes: H04B 7/0617 for beam forming
• CPC Class Codes: H04B 7/0619 using feedback from receiving side feedback signaling for adaptive modulation/coding H04L1/0001
• CPC Class Codes: H04B 7/063 Parameters other than those covered in groups H04B7/0623 - H04B7/0634, e.g. channel matrix rank or transmit mode selection
• CPC Class Codes: H04B 7/0695 using beam selection
• CPC Class Codes: H04B 7/088 using beam selection
• CPC Class Codes: H04L 1/1864 ARQ related signaling H04L1/1607 takes precedence
• CPC Class Codes: H04L 2025/03426 transmission using multiple-input and multiple-output channels
• CPC Class Codes: H04L 25/0204 of multiple channels
• CPC Class Codes: H04L 25/03305 Joint sequence estimation and interference removal joint detection of several desired signals H04L25/03331
• CPC Class Codes: H04L 47/803 Application aware
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04L 5/0007 the frequencies being orthogonal, e.g. OFDM(A), DMT
• CPC Class Codes: H04L 5/0048 Allocation of pilot signals, i.e. of signals known to the receiver
• CPC Class Codes: H04L 5/0051 of dedicated pilots, i.e. pilots destined for a single user or terminal
• CPC Class Codes: H04L 5/0053 Allocation of signaling, i.e. of overhead other than pilot signals
• CPC Class Codes: H04L 5/0055 Physical resource allocation for ACK/NACK for physical mapping arrangements in ARQ protocols H04L1/1861
• CPC Class Codes: H04L 5/0057 Physical resource allocation for CQI
• CPC Class Codes: H04L 5/0085 : when channel conditions change
• CPC Class Codes: H04L 5/1469 : using time-sharing
• CPC Class Codes: H04L 65/613 : for the control of the source by the destination control signals issued by the client directed to the server or network components specially adapted for selective content distribution H04N21/637
• CPC Class Codes: H04L 65/65 : Network streaming protocols, e.g. real-time transport protocol [RTP] or real-time control protocol [RTCP]
• CPC Class Codes: H04L 65/70 : Media network packetisation
• CPC Class Codes: H04L 65/75 : Media network packet handling
• CPC Class Codes: H04L 65/752 : adapting media to network capabilities
• CPC Class Codes: H04L 65/756 : adapting media to device capabilities
• CPC Class Codes: H04L 65/762 : at the source reformatting of additional data in video distribution servers H04N21/2355
• CPC Class Codes: H04L 65/764 : at the destination reformatting of additional data in video clients H04N21/4355
• CPC Class Codes: H04L 65/80 : Responding to QoS
• CPC Class Codes: H04M 1/72457 : according to geographic location
• CPC Class Codes: H04N 21/2402 : Monitoring of the downstream path of the transmission network, e.g. bandwidth available traffic monitoring in data switching networks H04L43/00; monitoring data switching networks utilization H04L43/0876
• CPC Class Codes: H04N 21/8456 : by decomposing the content in the time domain, e.g. in time segments
• CPC Class Codes: H04N 21/8543 : using a description language, e.g. Multimedia and Hypermedia information coding Expert Group [MHEG], eXtensible Markup Language [XML] information retrieval of semistructured data, the underlying structure being taken into account, e.g. mark-up language structure data G06F16/80
• CPC Class Codes: H04W 24/00 : Supervisory, monitoring or testing arrangements
• CPC Class Codes: H04W 24/02 : Arrangements for optimising operational condition
• CPC Class Codes: H04W 28/02 : Traffic management, e.g. flow control or congestion control
• CPC Class Codes: H04W 28/0226 : based on location or mobility handoff or reselection H04W36/00; mobile application services making use of the location of users or terminals H04W4/02
• CPC Class Codes: H04W 28/0289 : Congestion control load shedding arrangements in network planning H04W16/08; performing reselection for handling the traffic H04W36/22; wireless traffic scheduling H04W72/12
• CPC Class Codes: H04W 28/082 : among bearers or channels
• CPC Class Codes: H04W 28/20 : Negotiating bandwidth
• CPC Class Codes: H04W 36/0011 : for data sessions of end-to-end connection
• CPC Class Codes: H04W 36/0022 : for transferring data sessions between adjacent core network technologies
• CPC Class Codes: H04W 36/0072 : of resource information of target access point
• CPC Class Codes: H04W 36/08 : Reselecting an access point
• CPC Class Codes: H04W 36/125 : involving different types of service backbones
• CPC Class Codes: H04W 36/22 : for handling the traffic
• CPC Class Codes: H04W 36/26 : by agreed or negotiated communication parameters
• CPC Class Codes: H04W 4/021 : Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 48/06 : based on traffic conditions
• CPC Class Codes: H04W 48/12 : using downlink control channel
• CPC Class Codes: H04W 48/16 : Discovering, processing access restriction or access information
• CPC Class Codes: H04W 48/18 : Selecting a network or a communication service
• CPC Class Codes: H04W 56/001 : Synchronization between nodes
• CPC Class Codes: H04W 72/0446 : Resources in time domain, e.g. slots or frames
• CPC Class Codes: H04W 72/046 : the resource being in the space domain, e.g. beams
• CPC Class Codes: H04W 72/541 : using the level of interference
• CPC Class Codes: H04W 76/15 : Setup of multiple wireless link connections
• CPC Class Codes: H04W 8/02 : Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks
• CPC Class Codes: H04W 8/06 : Registration at serving network Location Register, VLR or user mobility server
• CPC Class Codes: H04W 8/082 : for traffic bypassing of mobility servers, e.g. location registers, home PLMNs or home agents
• CPC Class Codes: H04W 84/042 : Public Land Mobile systems, e.g. cellular systems
• CPC Class Codes: H04W 84/045 : using private Base Stations, e.g. femto Base Stations, home Node B
• CPC Class Codes: H04W 84/12 : WLAN [Wireless Local Area Networks]
• CPC Class Codes: H04W 88/02 : Terminal devices
• CPC Class Codes: H04W 88/08 : Access point devices
• CPC Class Codes: H04W 88/10 : adapted for operation in multiple networks, e.g. multi-mode access points
• CPC Class Codes: Y02D 30/70 : in wireless communication networks

Abstract

Technology to provide hybrid beamforming feedback is disclosed. In an example, a user equipment (UE) can include computer circuitry configured to: Receive a reference signal (RS) from a node; calculate an optimal channel direction from the RS; calculate an optimal signal-to-interference-plus-noise ratio (SINR) for the optimal channel direction, where the optimal SINR is conditionally calculated with an intra-cell interference component or calculated without the intra-cell interference component based on a feedback configuration; and transmit the optimal channel direction and the optimal SINR to the node.

Claims

1. A user equipment (UE) operable to provide hybrid beamforming feedback, having computer circuitry configured to:

receive a reference signal (RS) from a node;

calculate an optimal channel direction from the RS;

calculate an optimal signal-to-interference-plus-noise ratio (SINR) for the optimal channel direction, wherein the optimal SINR is conditionally calculated with an intra-cell interference component or calculated without the intra-cell interference component based on a feedback configuration; and

transmit the optimal channel direction and the optimal SINR to the node.

2. The computer circuitry of claim 1, wherein:

the optimal SINR calculated with the intra-cell interference component is represented by

3. The computer circuitry of claim 1, wherein the optimal SINR calculated with the intra-cell interference component is calculated assuming a simple random beamforming (RBF) linear precoding mechanism, and the optimal SINR calculated without the intra-cell interference component is calculated assuming a zero-forcing beamforming (ZFBF) linear precoding mechanism.

4. The computer circuitry of claim 1, wherein computer circuitry configured to transmit the optimal SINR is further configured to transmit both the optimal SINR calculated with the intra-cell interference component and the optimal SINR calculated without the intra-cell interference component.

5. The computer circuitry of claim 1, wherein computer circuitry configured to transmit the optimal channel direction and the optimal SINR is further configured to:

transmit a course quantization optimal channel direction and a course quantization optimal SINR calculated with the intra-cell interference component;

receive a request from the node to use fine quantization; and

transmit a fine quantization optimal channel direction and a fine quantization optimal SINR calculated without the intra-cell interference component.

6. The computer circuitry of claim 1, wherein computer circuitry configured to transmit the optimal channel direction and the optimal SINR is further configured to:

transmit a course quantization optimal channel direction, a pessimistic course quantization optimal SINR calculated with the intra-cell interference component, and an optimistic course quantization optimal SINR calculated without the intra-cell interference component;

receive a request from the node to use fine quantization; and

transmit a fine quantization optimal channel direction, a pessimistic fine quantization optimal SINR calculated with the intra-cell interference component, and an optimistic fine quantization optimal SINR calculated without an intra-cell interference component.

7. A method for receiving random beamforming (RBF) feedback and zero-forcing beamforming (ZFBF) feedback at a node, comprising:

receiving an optimal channel direction from a user equipment (UE);

receiving an optimal signal-to-interference-plus-noise ratio (SINR) for the optimal channel direction, wherein the node is configured to conditionally receive the optimal SINR calculated with an intra-cell interference component or receive the optimal SINR calculated without an intra-cell interference component based on a feedback configuration; and

linear precoding transmissions using RBF precoding or ZFBF precoding based on the optimal SINR.

8. The method of claim 7, further comprising:

transmitting a reference signal (RS) to the UE, wherein the optimal channel direction and the optimal SINR is determined from the RS.

9. The method of claim 7, further comprising:

selecting a RBF UE for a RBF group based on a criteria, wherein the criteria improves a channel condition, maximizes overall throughput, maximizes per user throughput, or minimizes delay; and

selecting a ZFBF UE for a ZFBF group based on the criteria;

wherein linear precoding transmissions further comprises;

linear precoding transmissions of the RBF UE in the RBF group using the RBF precoding; and

linear precoding transmissions of the ZFBF UE in the ZFBF group using the ZFBF precoding.

10. The method of claim 7, wherein receiving the optimal SINR for the optimal channel direction further comprises:

receiving both a pessimistic channel quality indicator (CQI) representing the optimal SINR calculated with the intra-cell interference component and an optimistic CQI representing the optimal SINR calculated without the intra-cell interference component.

11. The method of claim 7, wherein receiving the optimal SINR for the optimal channel direction further comprises:

receiving a course quantization precoding matrix indicator (PMI) representing a course quantization optimal channel direction and receiving a course quantization channel quality indicator (CQI) representing a course quantization optimal SINR calculated with the intra-cell interference component, wherein the course quantization PMI and the course quantization CQI are generated from a low density codebook;

evaluating the course quantization optimal SINR against an environmental condition or a system criteria;

transmitting a request to the UE to use fine quantization based on the course quantization optimal SINR; and

receiving a fine quantization PMI representing a fine quantization optimal channel direction and receiving a fine quantization CQI representing a fine quantization optimal SINR calculated without the intra-cell interference component, wherein the fine quantization PMI and the fine quantization CQI are generated from a high density codebook.

12. The method of claim 11, wherein linear precoding transmissions further comprises:

transmitting data using the RBF precoding for a RBF UE when a specified environmental condition threshold and a specified system criteria is not met; and

transmitting data using the ZFBF precoding for a ZFBF UE when the specified environmental condition threshold or the specified system criteria is met.

13. The method of claim 7, wherein receiving the optimal SINR for the optimal channel direction further comprises:

receiving a course quantization precoding matrix indicator (PMI) representing a course quantization optimal channel direction, receiving a pessimistic course quantization channel quality indicator (CQI) representing a pessimistic course quantization optimal SINR calculated with the intra-cell interference component, and receiving an optimistic course quantization CQI representing an optimistic course quantization optimal SINR calculated without the intra-cell interference component, wherein the course quantization PMI, the pessimistic course quantization optimal SINR, and the optimistic course quantization CQI are generated from a low density codebook;

evaluating the course quantization optimal SINR against an environmental condition or a system criteria;

transmitting a request to the UE to use fine quantization based on the course quantization optimal SINR; and

receiving a fine quantization precoding matrix indicator (PMI) representing a fine quantization optimal channel direction, receiving a pessimistic fine quantization CQI representing a pessimistic fine quantization optimal SINR calculated with the intra-cell interference component, and receiving an optimistic fine quantization CQI representing an optimistic fine quantization optimal SINR calculated without the intra-cell interference component, wherein the fine quantization PMI, the pessimistic fine quantization optimal SINR, and the optimistic fine quantization CQI are generated from a high density codebook.

14. The method of claim 13, wherein linear precoding transmissions further comprises:

transmitting data using the RBF precoding based on a pessimistic optimal SINR when a specified environmental condition threshold and a specified system criteria is not met; and

transmitting data using the ZFBF precoding based on an optimistic optimal SINR when the specified environmental condition threshold or the specified system criteria is met.

15. The method of claim 7, wherein:

the optimal SINR calculated with the intra-cell interference component is represented

16. The method of claim 7, wherein the node includes M transmit antennas, where M>

8 and the node is selected from the group consisting of a base station (BS), a Node B (NB), an evolved Node B (eNB), a baseband unit (BBU), a remote radio head (RRH), a remote radio equipment (RRE), a remote radio unit (RRU), and a central processing module (CPM).

17. At least one non-transitory machine readable storage medium comprising a plurality of instructions adapted to be executed to implement the method of claim 7.

18. A user equipment (UE) configured for hybrid beamforming feedback, comprising:

a transceiver to receive a data transmission from an evolved Node B (eNB); and

a processor to;

calculate an optimal channel direction from the data transmission;

generate a precoding matrix indicator (PMI) based on the optimal channel direction;

calculate an optimal signal-to-interference-plus-noise ratio (SINR) for the optimal channel direction, wherein the optimal SINR is conditionally calculated with an intra-cell interference component or calculated without the intra-cell interference component based on a feedback configuration;

generate a pessimistic channel quality indicator (CQI) when the optimal SINR is calculated with the intra-cell interference component; and

generate an optimistic CQI when the optimal SINR is calculated without the intra-cell interference component; and

wherein the transceiver is further configured to;

transmit the PMI and the pessimistic CQI to the eNB;

or

transmit the PMI and the optimistic CQI to the eNB.

19. The UE of claim 18, wherein the PMI is represented by φ

_n*, the pessimistic CQI (CQI₁) is represented by

20. The UE of claim 18, wherein the transceiver is further configured to transmit both the pessimistic CQI and the optimistic CQI, where the PMI is represented by φ

_n*, the pessimistic CQI (CQI₁) is represented by

21. The UE of claim 18, wherein the processor is further configured to:

generate a course quantization PMI and a course quantization CQI based on a low density codebook; and

generate a fine quantization PMI and a fine quantization CQI based on a high density codebook,

where the course quantization PMI (PMI₁) is represented by φ

_n*, the course quantization CQI (CQI₁) uses the pessimistic CQI and is represented by

22. The UE of claim 18, wherein the processor is further configured to:

generate a course quantization PMI, a pessimistic course quantization CQI, and an optimistic course quantization CQI based on a low density codebook; and

generate a fine quantization PMI, a pessimistic fine quantization CQI, and an optimistic fine quantization CQI based on a high density codebook,

where the course quantization PMI (PMI₁) is represented by φ

_n*, the pessimistic course quantization CQI (CQI₁) is represented by

23. The UE of claim 18, wherein the UE includes an antenna, a touch sensitive display screen, a speaker, a microphone, a graphics processor, an application processor, internal memory, or a non-volatile memory port.