Method for compensation of phase noise effect on data transmission in radio channel

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First Claim
1. A method for estimation and compensation of phase noise effect on data transmission comprising:
 a. Reception of a sequence of multiple signal samples;
b. Estimation of phase noise in the sequence of multiple signal samples;
c. Compensation of the phase noise in the sequence of multiple signal samples using a phase noise estimate,wherein the phase noise estimation comprises the successive steps of;
b1. Selection of a sequence of several signal samples from a variety of signal samples;
b2. Direct estimation of phase noise realization from the sequence of several signal samples;
b3. Generation of a sequence of estimates of the phase noise realization;
b4. Estimation and extraction of one or several phase noise lowfrequency spectral components by a linear combination of the phase noise realization estimates with weighted coefficients;
b5. Estimation of the phase noise in a sequence of multiple signal samples in a time domain using an inverse Fourier transform of the estimated lowfrequency phase noise components.
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Abstract
The present invention generally relates to the field of electrical communication and more specifically to apparatuses and methods of phase noise mitigation for signal transmission in wideband telecommunication systems.
The method for compensation of the phase noise effect on the data transmission through a radio channel is based on a possibility to present the phase noise of a reference oscillator like a random process where the main spectral density is concentrated in the lowfrequency region. Therefore, the number of estimated parameters can be reduced many times to several lowfrequency spectral components instead of a direct estimation in the time domain.
The advantage of the method is an improvement of the estimation accuracy and a reduction of the computational complexity.
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Current Assignee
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14 Claims
 1. A method for estimation and compensation of phase noise effect on data transmission comprising:
a. Reception of a sequence of multiple signal samples; b. Estimation of phase noise in the sequence of multiple signal samples; c. Compensation of the phase noise in the sequence of multiple signal samples using a phase noise estimate, wherein the phase noise estimation comprises the successive steps of; b1. Selection of a sequence of several signal samples from a variety of signal samples; b2. Direct estimation of phase noise realization from the sequence of several signal samples; b3. Generation of a sequence of estimates of the phase noise realization; b4. Estimation and extraction of one or several phase noise lowfrequency spectral components by a linear combination of the phase noise realization estimates with weighted coefficients; b5. Estimation of the phase noise in a sequence of multiple signal samples in a time domain using an inverse Fourier transform of the estimated lowfrequency phase noise components.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
1 Specification
The present application is a Continuation of PCT/RU2017/050072, filed on Aug. 7, 2017, entitled “METHOD OF COMPENSATING FOR THE EFFECT OF PHASE NOISE ON DATA TRANSMISSION OVER A RADIOFREQUENCY CHANNEL”, which claims convention priority to Russian Patent Application 2016133024, filed on Aug. 10, 2016, the entirety of which is incorporated herein by reference.
The present invention generally relates to the field of electrical communication and more specifically to apparatuses and methods of phase noise mitigation for signal transmission in broadband telecommunication systems.
The phase noise is an inherent characteristic of any signal generator which consists in a random uncontrolled variation of a generated signal phase in time. In the case of a communication system the phase noise of the reference oscillators at the transmitter and receiver sides leads to uncontrolled changes in the received signal phase in time which has to be estimated and compensated to provide reliable and valid reception of the transmitted data.
The phase noise is typically significant in oscillators based on semiconductor integrated circuits that are widely applied in modern wireless systems due to their low cost, small size and low power consumption. Wherein a phase noise level is higher for high frequency oscillators, e.g. for 6090 GHz millimeterwave band communication systems demonstrating an increasing growth of usage.
A basic scheme of data transmission in a wireless communication network is a single carrier scheme where information bits are transmitted with their modulation to timedomain signal symbols. A general scheme of a digital communication system with a single carrier is presented in
The described traditional method of the phase noise compensation in single carrier communication systems requires phase noise estimation for each transmitted symbol (using data symbols and a decisiondirected approach based on the demodulator output) or for a significant part of the symbols (using pilot symbols in the transmitted signal) for its efficient operation.
Besides, using phase information obtained from the demodulated data may typically be insufficiently reliable (i.e. the phase estimates may contain a sufficiently high level of errors) and the pilot samples known to the receiver may stand apart from each other by a significant number of signal samples. For example, insertion of pilot signals only to form guard cyclic prefixes is common in Single Carrier systems with Frequency Domain Equalization (SCFDE). This modulation type assumes block data transmission and is similar to Orthogonal Frequency Division Multiplexing (OFDM). The SCFDE modulation type is widespread in modern wireless communication systems.
For the indicated case of a sufficient time interval between the pilot signals the phase error estimation can be done using the known pilot signals and then the phase errors can be approximated for signal samples carrying the data using a constant or linear interpolation. However, such phase noise estimation may be not enough accurate, since the phase noise realization can significantly deviate from a constant value or a linear trend. Therefore, there is a need for a more accurate method for estimation and approximation of the phase noise realizations between the values estimated from the pilot signals. In particular, values of the phase noise realization at different signal samples are not independent but are correlated in time that can be used to improve an estimation accuracy.
A method for reduction of phase noise impact on a quality of received signal in single carrier systems is disclosed in U.S. Pat. No. 7,409,024 “Process for providing a pilot aided phase synchronization of carrier” published Aug. 5, 2008. A general structure of the apparatus disclosed in the patent is presented in
The prior art includes another technical solution presented in U.S. Pat. No. 7,733,993 “High speed gain and phase recovery in presence of phase noise” and providing a method of phase noise compensation in OFDM systems. The method disclosed in the patent assumes estimation of subcarrier phase distortions for an OFDM block in the frequency domain based on knowledge of pilot samples and application of hard decisions for the data subcarriers, conversion of phase error estimates from the frequency domain to the time domain via the Discrete Fourier Transformation (DFT) and smoothing of the obtained estimates via the Kalman filtering (
The priorart also includes a phase noise mitigation method for single carrier data transmission systems disclosed in U.S. Pat. No. 9,160,382 “Phase noise mitigation for wireless communications”. The method described in the patent is considered as a prototype of the present invention. It assumes estimation of phase noise distortions for each sample of the received signal based on knowledge of the pilot samples and usage of hard decisions for the data symbols, filtering of the estimated phase errors for each sample using the Joint Forward Backward Linear Prediction Filter (JFBLPF) and compensation of the phase noise by the filtered smoothed sequence of the phase errors. In order to improve the phase noise compensation efficiency, iterative execution of the method is additionally proposed to get more accurate estimates of the phase distortions for the data signal samples. A general scheme of a receiver using an apparatus described in U.S. Pat. No. 9,160,382 is presented in
Thus, there is a necessity for a method of phase noise estimation and compensation in wireless communication systems where pilot signals (or groups of pilot signals) known to the receiver are separated by sufficient time intervals, so that a realization of the phase noise cannot be accurately approximated by a constant or a linear trend. At the same time a priori known statistical characteristics of reference oscillators should be used for the phase noise estimation and estimation accuracy improvement, for example, the power spectral density of the phase noise or its derivative characteristics. This method of the phase noise suppression should have a relatively low computational complexity compared to analogs from the prior art that can provide a possibility of its implementation in FPGA or ASIC with limited hardware resources.
A purpose of the present invention is to develop an efficient method for phase noise estimation and compensation in the received signal for applications in modern single carrier wideband communication systems.
The developed method consists in estimation of a phase noise realization from a sequence of several symbols of the received signal using a priori known statistical characteristics of the reference oscillator and in compensation of the phase distortions for a sequence of multiple symbols of the signal with the obtained phase noise estimate.
The advantage of the developed method is, on one hand, an improvement of the estimation accuracy compared to known methods used in single carrier systems and, on the other hand, a reduction of the computational complexity. The latter result is provided by independence of the computational complexity of the length of the sequence of signal samples used for the phase noise estimation, in contrast to similar methods known from the prior art.
The advantage is achieved due to estimation of the frequency domain phase noise characteristics instead of the time domain characteristics which is based on a direct estimation of the phase distortions in a sequence of several received signal samples from the total set of samples of the received signal. As it is known from the prior art, most of the phase noise power produced by a reference oscillator is concentrated in the low frequency region, therefore, a relatively low number of the spectrum components is to be estimated to achieve a sufficient estimation accuracy. This number is independent of the number of signal samples used for the estimation. Calculation of the required lowfrequency phase noise spectrum characteristics from a sequence of signal samples is performed via a linear combination of the phase noise estimates with weighted coefficients. The estimated values of the spectral components are further used for calculation of a phase noise estimate for a sequence of multiple signal samples in the time domain by the inverse Fourier transform.
Therefore, a method for estimation and compensation of the phase noise effect on the data transmission has been developed and includes the following steps: (a) reception of a sequence of multiple signal samples; (b) estimation of the phase noise in the sequence of multiple signal samples; (c) compensation of the phase noise in the sequence of multiple signal samples using the phase noise estimate wherein the procedure of phase noise estimation comprises the following successive steps: (b1) selection of a sequence of several signal samples from a variety of signal samples; (b2) direct estimation of a phase noise realization in the sequence of several signal samples; (b3) generation of a sequence of estimates of the phase noise realization; (b4) estimation and extraction of one or several phase noise lowfrequency spectral components by a linear combination of the phase noise realization estimates with weighted coefficients; (b5) estimation of the phase noise in a sequence of multiple samples in the time domain using the inverse Fourier transform of the estimated lowfrequency phase noise components.
In one embodiment of the method, the number of estimated spectral components is selected a priori to cover the phase noise spectrum of the used signal generators at a specified level which is determined according to parameters of the data transmission and characteristics of the reference oscillator.
In another embodiment of the method, pilot signals a priori known to the receiver are used for direct estimation of the phase noise realization. The pilotonlybased estimation of the phase noise is the most efficient solution for the data transmission in a condition of a high level of noise in the received signal when a correct demodulation of the data symbols is impossible.
In another embodiment of the method, the received and demodulated data symbols are used for a direct estimation of the phase noise realization. This allows to increase the number of estimates of the phase noise realization and, as a result, to improve the phase noise estimation for the entire signal when the noise level allows a correct demodulation for the majority of the data samples.
In one more embodiment of the method, a combination of the pilot signals a priori known to the receiver and received and demodulated data samples are used for a direct estimation of the phase noise realization. In this case the phase error detection can be performed in a wide range of the signaltonoise ratios, on one hand, providing a reliable estimation due to selection of only the pilot samples in the high noise conditions and, on the other hand, increasing the number of the timedomain phase noise estimates in the case of correct demodulation for the majority of the data samples. Making decisions on usage of the phase noise estimates from the data samples can be based on a measured signaltonoise ratio of the received signal.
In one more embodiment of the method, the Fast Fourier Transform (FFT) is used for estimation of the phase noise spectral components. This transform is a special case of a linear combination of the estimates of the phase noise with weighted coefficients. This approach can be used in absence of a priori information about the correlation properties of the spectral components of the reference oscillator phase noise. This embodiment is more universal but demonstrates lower phase noise suppression efficiency compared to other possible embodiments of the proposed method.
In another embodiment, a method operating according to the minimum mean square error (MMSE) criterion is used for estimation of the phase noise spectral components. The estimation accuracy is improved in this embodiment due to usage of additional a priori information about the statistical characteristics of the reference oscillator. In particular, autocorrelation properties of the phase noise spectral components are taken into account.
In particular embodiments of the method, the phase noise is preliminarily estimated and compensated via a linear interpolation of the phase noise values between samples or groups of samples of the pilot signals a priori known to the receiver. Correctness of the phase noise estimation from the demodulated data signals is improved in that case and the method becomes applicable at a lower level of the signaltonoise ratio.
In another embodiment of the method, the mean value of the phase noise calculated via averaging of phase noise values over samples of the pilot signals a priori known to the receiver is preliminarily estimated and compensated. This approach also improves the phase noise estimation of the demodulated data of the received signal and has the lowest complexity among the available methods for the preliminary phase noise compensation as far as all samples are compensated with a single value.
In one more embodiment of the method, the phase noise estimation and compensation are performed before the received signal equalization. This approach is efficient in the case of the frequencydomain equalization because a level of the intercarrier interference due to a loss of the orthogonality of the equalized signal spectral components is low in that case. However, the intersymbol interference of the received signal leads to a degradation of the phase error estimation accuracy. In most of the cases, this estimation can be made only from the pilot signal samples because demodulation of the data symbols would lead to a large number of errors.
In another particular embodiment, the phase noise estimation and compensation are performed after the received signal equalization. An advantage of this approach consists in a possibility to perform the phase noise estimation from all signal samples using both the known pilot samples and the demodulated data symbols and it increases the accuracy of the spectral component estimation and phase noise compensation efficiency for the entire signal.
In an embodiment of the method, a block modulation with a single carrier and frequency domain equalization is used for the signal transmission. This embodiment is applicable in modern wireless communication systems such as IEEE 802.11ad, LTE and many other cases with the SCFDE modulation. In this embodiment a length of the set of signal samples used for the phase noise estimation and compensation is a multiple of the signal modulation block length. This approach also optimizes the computational complexity.
Further features and advantages of the present invention will become apparent from the following description of the preferred embodiments with reference to accompanying drawings.
The following reference numerals are used in the figures:
 100—a single carrier communication system
 101—a forward error correction encoder,
 102—a digital modulator,
 103—a digitaltoanalog convertor,
 104—a transmitter mixer,
 105—a power amplifier,
 106—a transmitter antenna,
 107—a radio channel,
 108—a receiver antenna,
 109—a lownoise amplifier,
 110—a receiver mixer,
 111—an analogtodigital converter,
 112—a frequency and time synchronization module,
 113—an equalizer,
 114—a phase errors compensator,
 115—a digital demodulator,
 116—a lowpass filter,
 117—a phaselocked loop,
 118—a forward error correction decoder,
 200—a single carrier communication system,
 300—a single carrier receiver,
 301—a frequency and time synchronization module,
 302—an equalizer,
 303—a phase noise estimation and compensation module,
 304—a demodulator,
 305—a decoder,
 400—a single carrier receiver,
 500—a phase noise estimation and compensation module,
 501—a signal delay line,
 502—a phase error detector,
 503—a phase noise spectral components estimator,
 504—an inverse Fourier Transform module,
 505—a phase error compensator,
 600—a phase noise estimation and compensation apparatus,
 601—a signal delay line,
 602—a preliminary phase noise estimator,
 603—a preliminary phase noise compensator,
 604—a phase error detector,
 605—a phase noise spectral components estimator,
 606—an inverse Fourier Transform module,
 607—a signal delay line,
 608—a phase error compensator
The present invention proposes a method for phase noise suppression in the received signal for data transmission in wireless communication systems which is characterized by a better efficiency and a lower computational complexity in comparison with known methods based on a linear interpolation or phase error averaging.
The method is based on a possibility to present the phase noise of a reference oscillator like a random process where the main spectral density is concentrated in the lowfrequency region. Therefore, due to estimation of a phase noise as several samples of the received signal in the frequency domain instead of the time domain, the number of estimated parameters can be reduced many times to several lowfrequency spectral components instead of a direct estimation of the time domain realization.
For a detailed description of the developed method, a mathematical model of the data transmission is considered where a signal s0(n) generated at the transmitter propagates through a channel with the additive white Gaussian noise ω(n) (AWGN) and is received at the receiver in presence of a phase noise of the reference oscillator φ(n), which causes phase changes for each received signal sample:
x(n)=s(n)·exp[jφ(n)]+ω(n) (1)
The main goal of efficient phase noise suppression is to obtain the most accurate estimate of the time domain realization of φ(n) for each sample of the signal and to apply the obtained estimate for compensation of the received signal phase distortions. In the claimed invention estimation of the phase noise is provided by several samples constituting a subset with the length of N from the total number L of signal samples, observed at the receiver (N≤L). In the general case, selection of the signal samples for the phase noise estimation by the proposed method can be done arbitrarily so it is not necessary to select all values of the observed signal or sampling with any period. Let the indices of the signal samples that are included into the subset of samples form a set of I_{n}={i,i∈[0,L−1]} with the power of N. Then, the values included into the subset of samples of the received signal and can be represented in a vector form as:
x=SΦ+Ω (2)
where X=[{x(m), m∈I_{n}}]^{T }is a vector of the received signal samples included into the current subset, Φ[{e^{jφ(m)}}, m∈I_{n}}]^{T }is a vector of a phase noise time domain realization, Ω=[{ω(m), m∈I_{n}}]^{T }is a vector of Gaussian noise samples, S=diag[{s(m), m∈I_{n}}]^{T}) is a diagonal matrix in which nonzero elements of the main diagonal correspond to the transmitted signal samples from the subset.
A frequency characteristic of the phase noise can be calculated from a subset of N samples of the entire phase noise realization of L signal samples. It is provided by the discrete Fourier transform (DFT) of the samples of the phase noise realization in the time domain corresponding to the signal elements and included into the subset:
where J=[J(0) . . . J(L−1)] is a vector of the phase noise spectral characteristic for the entire observed signal, F is an L×N shortened DFT matrix containing only the columns of the DFT matrix corresponding to the indices of the signal samples from the subset I_{n}.
In a vector form the frequency response of the phase noise realization for the entire observed signal can be represented as a sum of two components:
J=J_{u}+J_{−u} (4)
where J_{u}=[J(0) . . . J(W/2−1),0, . . . 0, J (L−W/2), J(L−1)]^{T }is a vector of lowfrequency spectral components of the phase noise realization, and J_{−u}=[0, . . . 0, J (W/2), J (L−W/2−1), 0 . . . 0]^{T }is a vector of highfrequency spectral components of the phase noise realization.
Due to the lowfrequency nature of the phase noise, estimation of its spectral characteristics is provided by determining only the values of the vector Ju, and the vector Ju can be assumed equal to zero. The number of the estimated spectral components of the phase noise W (the length of the vector Ju) is chosen to cover the phase noise spectrum at a certain level. Characteristics of the phase noise power spectral density can be determined according to characteristics of the reference oscillator.
The number of lowfrequency spectral components W covering the required spectral region of the phase noise depends on a ratio of a duration of the subset of N samples of the signal to the received signal sample period. However, in most of the practical implementations the number W is chosen to be equal to 3, 5, 7.
Estimation of the phase noise spectral components for the entire signal includes phase error detection from each sample of the subset of the received signal samples and forming a sequence of estimates of a time domain phase noise realization Y for the considered subset X, which is defined as:
Y=(S)^{−1}·X=F^{H}·J_{u}+F^{H}·J_{−u}+(S)^{−1}Ω (5)
Phase errors for each sample from the subset are calculated by dividing the received signal by the complex values of the transmitted signal samples (the diagonal elements of the matrix S). The transmitted signal samples can be determined both on a basis of a priori knowledge of pilot samples in the transmitted signal and from the received signal data samples by making hard decisions in the demodulator which consists in approximation of the received signal symbols by the signal constellation values situated at the shortest Euclidean distance. Phase error estimation of the data samples may be inaccurate due to demodulation errors leading to a degradation of the described method, however, this effect may be neglected for the purposes of the present description.
Expression (5) can be reduced to the form:
Y=F^{H}·J_{u}+E, (6)
where E is a modified additive Gaussian noise that includes the receiver Gaussian noise and the phase noise spectral components J_{u}, excluded from the further estimation.
E=F^{H}·J_{−u}+(S)^{−1}Ω (7)
Estimates of nonzero vector elements of the phase noise spectral components J_{u }can be calculated by filtering the sequence of estimates of the phase noise realization Y:
Ĵ_{u}=M·Y, (8)
where M is a matrix of the phase noise spectral characteristics estimation with the size of W×N. Ĵ_{u}=[Ĵ(0) . . . Ĵ(W/2−1), Ĵ(L−W/2), . . . Ĵ(L/2)] is a vector of estimated spectral components including only W nonzero elements of the vector J_{u}.
In one embodiment, estimates of the first W phase noise spectral components can be calculated by the Fourier transform from all N samples of the subset. In that case, the filtering matrix M has the size of W×N and is formed by rows of columns of the Fourier matrix F of the size of L×L, corresponding to the estimated spectral components and indices of the signal samples I_{n }included into the subset. This approach is the simplest way to calculate the required values but it does not consider autocorrelation properties of the phase noise spectral components and additionally requires the phase noise sampling to be periodical.
In another embodiment, the criterion of minimum mean square error (MMSE) is used for calculation of elements of the filtering matrix M according to the equation:
M=R_{JuJu}·
where R_{JuJu }is a correlation matrix of W lowfrequency phase noise spectral components, whose elements depend on characteristics of the carrier frequency reference oscillator, R_{εε} is a correlation matrix of residual noises,
An estimate of the time domain phase noise realization for all L samples of the received signal further used for the phase noise compensation can be calculated as an inverse discrete Fourier transform with the dimensions of W×L from estimates of the phase noise spectral components J_{u}:
Thus, a method of phase noise compensation in a received signal based on estimation of the spectral components presented in this invention comprises the following steps:
1. Selection of a sequence of N signal samples S from a variety of L received signal samples s(n), forming a subset from which the estimation is performed;
2. Estimation of the phase noise realization Y in the sequence of N signal samples included into the current subset using knowledge of the pilot samples and the demodulated data samples;
3. Estimation of phase noise lowfrequency spectral components Ĵ_{u }by a linear combination of the phase noise realization estimates Y with weighted filter coefficients M;
4. Calculation of an estimate of the timedomain phase noise realization for all L samples of the received signal via the inverse Fourier transform of the estimated phase noise lowfrequency components Ĵ_{u}.
5. Using this estimate for the phase noise compensation in the received signal s(n).
It should be noted that accuracy of the estimation of phase errors from the data samples strongly depends on a level of all noises in the received signal. Therefore, in order to improve the quality of the phase distortion estimation, a preliminary estimation and compensation of the phase noise for elimination of a common phase rotation of all signal samples included into the ith subset can be additionally performed. This procedure is based on estimation of phase errors from the pilot samples of the received signal, which are known a priori. In one embodiment, a linear interpolation of the phase values between the estimates is performed to calculate the preliminary eliminated phase error for the data symbols. In another embodiment, the phase noise compensation for the data samples is performed with the average value of the phase errors calculated from the pilot samples of the signal.
A general scheme of an embodiment of a single carrier receiver 300 used in singlecarrier communication systems containing a phase noise suppression scheme implementing the developed method is shown in
A possible embodiment of the present invention is a single carrier receiver architecture 400 shown in
A functional diagram of an embodiment of a digital phase noise estimation and compensation module 500 using the method presented above is shown in
In order to illustrate efficiency of the phase noise suppression, the proposed method was compared with the estimation and compensation schemes based on a linear interpolation and averaging of phase error estimates performed from pilot samples of the signal.
The Bit Error Rate (BER) is one of the main characteristics of a data transmission quality in communication systems and is defined as a ratio of the number of incorrectly received bits to the total number of received bits.
As can be seen from the presented examples of different phase noise compensation methods, in the case of absence of a phase noise suppression algorithm a complete system malfunction with a constant level of BER=0.08 is observed. The usage of the phase noise compensation for the two considered methods makes possible to achieve the BER level of 106. The gain of using the proposed method is 6 dB in terms of SNR, which is a significant advantage of this scheme compared to the liner interpolation method. In the claimed method a degradation of the SNR operating point due to the phase noise impact on the data transmission is only 1.2 dB at the level of BER 106 relative to the ideal case of absence of the phase noise. It is an acceptable value for modern communication systems.
Computational complexity of the proposed method requires W×N complex multiplications and 2×W×(N−1) additions for estimation of phase noise spectral components, as well as W×N complex multiplications and 2×(W−1)×N additions of the multiplication results for calculation of the inverse discrete Fourier transform (DFT). The methods of phase noise estimation and compensation known from the prior art, based on the digital filtering procedure of a sequence of received signal samples, require performing N×N complex multiplications and 2×N×(N−1) additions of the multiplication results to estimate a phase noise realization on a sequence of N symbols. Since the size of N of the sequence from which the estimation is done is much larger than the number of estimated spectral components W, the computational complexity of the known analogues significantly exceeds the complexity of the method proposed in the present invention.
The present invention is not limited to the embodiments disclosed in this description for illustrative purposes only and covers all modifications and variations that are not beyond the scope and essence of the invention as defined by the claims.