Method, apparatus and device for high order qam phase noise suppression

Abstract

The application provides a high-order QAM phase noise suppression method, device and equipment, and belongs to the technical field of signal transmission. The method comprises the following steps: after timing synchronization, Doppler compensation and frame synchronization processing are performed on a received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters by using pilot observation values in the pilot signal and the symbol decision error; and performing phase compensation on the non-pilot signal by using the adjusted Kalman filter parameters, predicting phase noise at a non-pilot time, and adjusting the Kalman filter parameters by using the pilot observation values and the symbol decision error, so that the method can better adapt to time-varying phase noise characteristics and effectively improve the high-order QAM phase noise suppression capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal transmission, in particular to a high-order QAM phase noise suppression method, device and equipment. BACKGROUND

[0002] The noise in a communication system is mainly divided into additive noise and multiplicative noise. The additive noise is represented by the additive white Gaussian noise in the channel, and the multiplicative noise is represented by the phase noise. From the perspective of the frequency source, the phase noise represents the frequency stability of the device. The long-term stability is mainly caused by the slow frequency change due to temperature and aging, and the short-term stability refers to the random and rapid phase fluctuation or frequency fluctuation.

[0003] For a single frequency signal source, its ideal state should be a spectrum with an infinitely narrow spectral line. In actual application, the spectrum of any signal is not absolutely pure, and the measurable spectral line has a certain width and has periodic interference or random phase offset. These interferences or offsets can be referred to as phase noise. In an actual system, the phase noise mainly comes from the frequency processing related modules, such as the local oscillator, the modulator, the demodulator, the frequency divider, the mixer, the up-converter and the down-converter; in addition, some other factors, such as the nonlinearity of the radio frequency module, the multipath fading and the Doppler effect of the channel, and the sampling frequency deviation in the system, can also introduce the phase noise. From the frequency domain, the main part of the phase noise is the noise in the range far from the carrier frequency, which is mainly the phase noise of the voltage-controlled oscillator; and in the range close to the carrier frequency, it is the phase noise of the reference signal source.

[0004] The phase noise of the local oscillator signal can cause the carrier frequency tracking accuracy and increase the system bit error rate. The higher the modulation order is, the worse the tolerance to the phase noise is. When the phase noise is too large, the delay of the loop processing of the traditional carrier phase tracking loop can cause the phase noise to be amplified by several times (the delay times the phase noise ), and for the high-order modulation system, the convergence or lock loss can easily occur.

[0005] Therefore, how to improve the suppression ability of the high-order QAM phase noise has become a technical problem to be solved by the person skilled in the art. SUMMARY

[0006] The present application provides a high-order QAM phase noise suppression method, device and equipment to solve the defects that the suppression ability of the high-order QAM phase noise is poor and the convergence or lock loss easily occurs in the prior art.

[0007] In a first aspect, the present application provides a high-order QAM phase noise suppression method, comprising:

[0008] After timing synchronization, Doppler compensation and frame synchronization processing of the received signal, a known pilot symbol block inserted at a preset interval is identified, and a pilot signal and a non-pilot signal in the received signal are extracted;

[0009] Phase compensation and hard decision are performed on the non-pilot signal to obtain a symbol decision error;

[0010] The pilot observation value in the pilot signal and the symbol decision error are used to adjust Kalman filter parameters;

[0011] The non-pilot signal is phase compensated using the adjusted Kalman filter parameters to predict phase noise at a non-pilot time.

[0012] According to the high-order QAM phase noise suppression method provided by the application, the phase compensation and hard decision performed on the non-pilot signal to obtain a symbol decision error comprises:

[0013] After the phase compensation and hard decision performed on the non-pilot signal, a feedback phase observation is calculated;

[0014] Based on the feedback phase observation and the predicted state parameter, a feedback observation state parameter error is determined as the symbol decision error.

[0015] According to the high-order QAM phase noise suppression method provided by the application, the pilot observation value in the pilot signal and the symbol decision error are used to adjust Kalman filter parameters, which comprises:

[0016] An estimated state parameter and an estimated error covariance matrix are initialized, and a predicted state parameter and a predicted error covariance matrix are calculated;

[0017] The predicted state parameter and the predicted error covariance matrix are updated using the pilot observation value in the pilot signal and the symbol decision error to adjust the Kalman filter parameters.

[0018] According to the high-order QAM phase noise suppression method provided by the application, the predicted state parameter and the predicted error covariance matrix are calculated, which comprises:

[0019] According to a state prediction equation, a state prediction parameter is obtained;

[0020] According to the estimated error covariance matrix, a predicted error covariance matrix is calculated.

[0021] According to the high-order QAM phase noise suppression method provided by the application, the estimated error covariance matrix is used to calculate the predicted error covariance matrix, which comprises:

[0022] The difference between the phase observation and the predicted state parameter is determined;

[0023] estimate noise covariance matrix online by using sliding window average through the difference between the phase observation and the predicted state parameter;

[0024] determine the prediction error covariance matrix by using the estimated noise covariance matrix and the state transition matrix.

[0025] According to the high-order QAM phase noise suppression method provided by the application, the adjustment of the Kalman filter parameter comprises:

[0026] ;

[0027] wherein, is a Kalman gain vector, is a prediction error covariance matrix, is an observation matrix, is a phase observation noise variance.

[0028] According to the high-order QAM phase noise suppression method provided by the application, the calculation of the feedback phase observation comprises:

[0029] determining a pilot block length;

[0030] calculating the feedback phase observation by using the same number of hard decision values as the pilot block length.

[0031] According to the high-order QAM phase noise suppression method provided by the application, the method further comprises:

[0032] determining a state parameter vector of the phase noise;

[0033] determining a state transition equation of the phase noise and the frequency offset based on the state parameter vector, a state transition matrix and a state transition process noise;

[0034] wherein, the state transition process noise is subject to a Gaussian distribution based on a phase noise variance and a frequency offset noise variance.

[0035] In the second aspect, the application provides a high-order QAM phase noise suppression device, comprising:

[0036] an extraction module, configured to identify a known pilot symbol block inserted at a preset interval after timing synchronization, Doppler compensation and frame synchronization processing of a received signal, and extract pilot signals and non-pilot signals in the received signal;

[0037] a feedback module, configured to perform phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors;

[0038] an adjustment module, configured to adjust Kalman filter parameters by using pilot observation values in the pilot signals and the symbol decision errors.

[0039] The compensation module is used to perform phase compensation on the non-pilot signal using the adjusted Kalman filter parameters to predict the phase noise at the non-pilot moment.

[0040] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the high-order QAM phase noise suppression method as described above is implemented.

[0041] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described high-order QAM phase noise suppression methods.

[0042] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described high-order QAM phase noise suppression methods.

[0043] The present invention provides a high-order QAM phase noise suppression method, device and equipment. The method includes: after performing timing synchronization, Doppler compensation and frame synchronization processing on a received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters using a pilot observation value and a symbol decision error in the pilot signal; performing phase compensation on the non-pilot signal using the adjusted Kalman filter parameters, and predicting the phase noise at the non-pilot moment; adjusting the Kalman filter parameters according to the pilot observation value and the symbol decision error, which can better adapt to the time-varying phase noise characteristics and effectively improve the suppression capability of high-order QAM phase noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 1 is a flow chart of a method for suppressing high-order QAM phase noise provided in this embodiment;

[0046] Figure 2 Schematic diagram of the principle of the high-order QAM phase noise suppression method provided in this embodiment;

[0047] Figure 3A flowchart of the Kalman filter parameter adjustment provided by the embodiment is shown in the figure;

[0048] Figure 4 A receiving schematic diagram at different damages during simulation test is shown in the figure;

[0049] Figure 5 A simulation result and a phase noise suppression effect of the prior art in the case of no noise are shown in the figure;

[0050] Figure 6 A simulation result and a phase noise suppression effect of the prior art in the case of noise are shown in the figure,

[0051] Figure 7 A structure schematic diagram of the high-order QAM phase noise suppression device provided by the embodiment is shown in the figure;

[0052] Figure 8 A structure schematic diagram of the electronic device provided by the embodiment is shown in the figure. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0054] Figure 1 A flowchart of the high-order QAM phase noise suppression method provided by the embodiment is shown in the figure.

[0055] As shown in the figure, the high-order QAM phase noise suppression method provided by the embodiment mainly includes the following steps: Figure 1

[0056] 101、After timing synchronization, Doppler compensation and frame synchronization processing of the received signal, the known pilot symbol block inserted at a preset interval is identified, and the pilot signal and the non-pilot signal in the received signal are extracted.

[0057] In a specific implementation process, the sending end first inserts a pilot into the signal. As shown in Table 1, the format after inserting the known pilot symbol block at a certain interval in the sending end is shown in the table.

[0058] Table 1

[0059]

[0060] ​The known pilot symbol block is inserted in the signal sent by the sending end, so that the received signal is a signal including the known pilot symbol block. After timing synchronization, Doppler compensation and frame synchronization processing are performed on the received signal, the received signal is extracted to obtain the pilot signal and the non-pilot signal.

[0061] 102. The non-pilot signal is phase compensated and hard-decision to obtain a symbol decision error.

[0062] As shown in Figure 2 Fig. 1 is a schematic diagram of a high-order QAM phase noise suppression method. After the pilot signal and the non-pilot signal are extracted, the non-pilot signal enters the phase compensation module, and then the hard decision module is fed back to track to obtain a symbol decision error input Kalman filter module. Specifically, after the non-pilot signal is phase compensated and hard-decision, the feedback phase observation is calculated, and based on the feedback phase observation and the predicted state parameter, the feedback observation state parameter error is determined as the symbol decision error.

[0063] 103. The pilot observation value in the pilot signal and the symbol decision error are used to adjust the Kalman filter parameter.

[0064] The Kalman filter parameter is adjusted by the forward correction and the feedback tracking of the pilot assistance, and the phase state is updated by using the pilot observation value and the symbol decision error, so that the effective adjustment of the Kalman filter parameter is completed.

[0065] The pilot point provides real phase information, which is used for the prediction result of the forward correction Kalman filter to improve the accuracy of the estimation. The phase noise of the non-pilot point needs to be predicted and compensated by the Kalman filter, and the phase error is extracted from the compensated symbol to correct the measurement result of the Kalman filter. Through the forward and backward correction, it is ensured that the corrected Kalman filter coefficient can improve the phase noise compensation accuracy and has dynamic tracking performance, so that the pilot overhead can be reduced.

[0066] 104. The non-pilot signal is phase compensated by using the adjusted Kalman filter parameter to predict the phase noise at the non-pilot time.

[0067] After the Kalman filter parameter is adjusted by the forward and backward adjustment, the non-pilot signal is phase compensated by using the finally adjusted Kalman filter parameter, that is, the received signal is phase rotated, the Kalman filter phase tracking is performed, and the phase noise at the non-pilot time is predicted.

[0068] Further, the present embodiment also includes a joint phase-frequency offset state model, which tracks the phase noise and the frequency offset by extending the state variable to avoid the accumulation of phase error caused by the coupling of the two. Specifically, the joint phase-frequency offset state space model is constructed by the following steps:

[0069] Determine the state parameter vector of phase noise. The dynamic characteristics of phase noise can be modeled as a Wiener process with frequency deviation. symbol state parameter vector Denoted as (1):

[0070] (1)

[0071] in, Indicates the The phase error of each symbol (rad), Indicates the The residual frequency deviation of the symbol (Hz).

[0072] Based on the state parameter vector, state transfer matrix and state transfer process noise, the state transfer equation of phase noise and frequency offset is determined, as shown in (2):

[0073] (2)

[0074] in, Indicates the The state parameter vector of symbols, the matrix is the state transfer matrix, is the fixed matrix, specifically (3):

[0075] (3)

[0076] in, is the symbol period, For the The state transition process noise of the symbol obeys the Gaussian distribution based on the phase noise variance and the frequency offset noise variance, that is, ,matrix is a 2×2 noise covariance matrix, expressed as (4):

[0077] (4)

[0078] in, 、 are the phase noise variance and frequency offset noise variance respectively.

[0079] Furthermore, based on the above embodiment, this embodiment uses the pilot observation value and symbol decision error in the pilot signal to adjust the Kalman filter parameters, including:

[0080] Initialize the estimated state parameters as (5):

[0081] (5)

[0082] The estimation error covariance matrix is initialized as (6):

[0083] (6)

[0084] After initialization, state prediction is performed, and the predicted state parameter of the kth symbol is obtained according to the state prediction equation as (7):

[0085] (7)

[0086] The predicted state parameter of the kth symbol is denoted as

[0087] The predicted error covariance matrix of the kth symbol is calculated according to the estimation error covariance matrix as (8):

[0088] (8)

[0089] wherein denotes the predicted error covariance matrix of the kth symbol, and the noise covariance matrix of the kth symbol is denoted as The noise covariance matrix is obtained through adaptive smoothing filtering, that is, the noise covariance matrix is estimated online through sliding window averaging to realize adaptive updating of the noise parameter, as shown in the following formula (9):

[0090] (9)

[0091] wherein is the estimation noise covariance matrix of the kth symbol, is a smoothing factor, is a Kalman gain vector, is an observation state parameter error, and the difference between the phase observation and the predicted state parameter is denoted as (10):

[0092] (10) wherein

[0093] is an observation matrix, and the design only observes the phase error, that is, .

[0094] The noisy phase observation is denoted as , and is represented as (11):

[0095] (11)

[0096] wherein,​​​​​​​​ The phase observation noise is subject to Gaussian distribution, i.e. , is the phase observation noise variance.

[0097] There are two methods to calculate the noisy phase observation. One is to calculate through pilot, denoted as feedforward phase observation . Assuming the pilot block length is , the ML (Maximum Likelihood) estimation of noisy phase observation is used at the pilot, i.e. (12):

[0098] (12)

[0099] wherein, is the received pilot symbol, is the transmitted pilot symbol, is the pilot block length, is the pilot symbol variable. The phase observation noise variance estimated by the formula is:

[0100] (13)

[0101] wherein, is the signal-to-noise ratio, i.e. the ratio of symbol energy to noise variance, which can be estimated at the pilot using the MMSE (Minimum Mean Square Error) algorithm.

[0102] The other way to calculate the noisy phase observation is to calculate through hard decision value, denoted as feedback phase observation . Similarly, the phase observation is calculated using hard decision values, as (14):

[0103] (14)

[0104] wherein, is the received symbol after phase compensation at non-pilot, is the hard decision value of the received symbol at non-pilot, i.e. the constellation point closest to the received symbol in the QAM constellation.

[0105] After completing the state prediction, the state update is performed, and the pilot observation value and symbol decision error (i.e. hard decision feedback value) in the pilot signal are used to update the predicted state parameter and predicted error covariance matrix, so as to adjust the Kalman filter parameter.

[0106] The estimated state parameter at the kth moment is updated as (15):

[0107] (15)

[0108] The estimated error covariance matrix is updated as (16):

[0109] (16)

[0110] in, is the Kalman gain vector, and the update process is (17)

[0111] (17)

[0112] in, is the Kalman gain vector, is the forecast error covariance matrix, is the observation matrix, is the phase observation noise variance.

[0113] Finally, phase compensation is performed according to the estimated state parameters at the kth moment , for the received signal at non-pilot Perform phase compensation, that is (18)

[0114] (18)

[0115] Figure 3 FIG. 1 is a flow chart of Kalman filter parameter adjustment according to the embodiment of the present invention.

[0116] like Figure 3 As shown, after frame synchronization processing, the input signal is extracted and divided into a pilot signal and a non-pilot signal. A feedforward phase observation is calculated for the pilot signal and combined with the predicted state parameters to determine the feedforward observation state parameter error. For the non-pilot signal, after phase compensation and hard decision, a feedback phase observation is calculated and combined with the predicted state parameters to determine the feedback observation state parameter error. The feedforward observation state parameter error on the pilot-assisted side and the feedback observation state parameter error on the feedback tracking side are processed to obtain the observation state parameter error. The noise covariance matrix and the prediction error covariance matrix are then calculated. The prediction error covariance matrix is ​​influenced by the estimation error covariance matrix. Finally, after adjusting the noise covariance matrix and the estimation error covariance matrix, the prediction error covariance matrix is ​​obtained. A new Kalman gain is calculated, phase compensated, and output. The Kalman gain is also adjusted by the signal-to-noise ratio estimate in the pilot signal. The adjusted Kalman gain is also influenced by the estimated state parameters, the state transition matrix, and the predicted state parameters to ensure the final phase compensation accuracy.

[0117] The adjustment of the Kalman filter parameters is realized by the pilot-assisted feedforward adjustment and the feedback adjustment of the feedback tracking side, and then the phase compensation is performed, which can estimate the noise covariance matrix and the phase observation noise variance in real time, and adapt to the time-varying phase noise characteristics (such as oscillator aging and temperature drift). It ensures that the high-order QAM system can realize high-precision phase noise suppression under low pilot overhead, and provides core technical support for high-throughput satellite communication.

[0118] In order to verify the effect of the technical scheme of the application, the following performance simulation and comparison are carried out, and the simulation parameters are as follows:

[0119] Modulation mode: 128QAM

[0120] Symbol rate: 750Msps

[0121] Phase noise model: -80 dBc / Hz@100Hz, -125dBc / Hz@10MHz

[0122] Residual frequency offset: 10 kHz

[0123] Pilot block length: 16 symbols

[0124] Pilot block interval: 5000 symbols

[0125] Signal-to-noise ratio: Eb / N0 = 20 dB

[0126] The simulation results are as follows:

[0127] Figure 4 The receiving schematic diagram under different damages is shown in a, b, c and d. It can be seen that due to the influence of phase noise and residual frequency offset, the received signal constellation diagram is rotated, thereby causing bit error.

[0128] Figure 5 The simulation results without noise and the phase noise suppression effect of the prior art are shown in a and b. The residual phase error of the prior art is 0.8° (RMS), and the residual phase error of the technical scheme of the application is 0.34° (RMS).

[0129] Figure 6 The simulation results with noise and the phase noise suppression effect of the prior art are shown in a and b. The residual phase error of the prior art is 2.8° (RMS), and the bit error rate is 4.95e-5. The residual phase error of the technical scheme of the application is 2.7° (RMS), and the bit error rate is 5.7e-6.

[0130] Compared with the prior art, the phase error is reduced by 57.5% after phase noise suppression in the case of no noise, and the BER is improved by one order of magnitude in the case of noise.

[0131] Based on the same general inventive concept, the application also protects a high-order QAM phase noise suppression device. The high-order QAM phase noise suppression device described below can be referred to in conjunction with the high-order QAM phase noise suppression method described above.

[0132] Figure 7 is a structural schematic diagram of the high-order QAM phase noise suppression device provided by the embodiment.

[0133] As shown in Figure 7 , the high-order QAM phase noise suppression device provided by the embodiment includes:

[0134] The extraction module 701 is configured to identify a known pilot symbol block inserted at a preset interval after timing synchronization, Doppler compensation and frame synchronization processing of the received signal, and extract a pilot signal and a non-pilot signal in the received signal.

[0135] The feedback module 702 is configured to perform phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error.

[0136] The adjustment module 703 is configured to adjust Kalman filter parameters by using a pilot observation value in the pilot signal and the symbol decision error.

[0137] The compensation module 704 is configured to perform phase compensation on the non-pilot signal by using the adjusted Kalman filter parameters to predict phase noise at a non-pilot time.

[0138] Figure 8 is a structural schematic diagram of the electronic device provided by the embodiment.

[0139] As shown in Figure 8As shown, the electronic device can include a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 complete mutual communication through the communications bus 840. The processor 810 can invoke a logic instruction in the memory 830 to execute a high-order QAM phase noise suppression method, which includes: after timing synchronization, Doppler compensation, and frame synchronization processing of a received signal, identifying a known pilot symbol block inserted at a preset interval, extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters using pilot observation values in the pilot signal and the symbol decision error; and performing phase compensation on the non-pilot signal using the adjusted Kalman filter parameters to predict phase noise at a non-pilot time.

[0140] In addition, the logic instruction in the memory 830 described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0141] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the high-order QAM phase noise suppression method provided by the above-mentioned methods, which includes: after timing synchronization, Doppler compensation, and frame synchronization processing of a received signal, identifying a known pilot symbol block inserted at a preset interval, extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters using pilot observation values in the pilot signal and the symbol decision error; and performing phase compensation on the non-pilot signal using the adjusted Kalman filter parameters to predict phase noise at a non-pilot time.

[0142] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the high-order QAM phase noise suppression method provided by the above method, and the method comprises: after timing synchronization, Doppler compensation and frame synchronization processing of a received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters using pilot observation values in the pilot signal and the symbol decision error; and performing phase compensation on the non-pilot signal using the adjusted Kalman filter parameters to predict the phase noise at a non-pilot time.

[0143] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0144] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0145] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for suppressing high-order QAM phase noise, characterized in that: include: After performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identifying known pilot symbol blocks inserted at preset intervals, and extracting pilot signals and non-pilot signals from the received signal; Performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error, including: calculating a feedback phase observation after performing phase compensation and hard decision on the non-pilot signal; determining a feedback observation state parameter error based on the feedback phase observation and the predicted state parameter as the symbol decision error; Adjusting Kalman filter parameters using the pilot observation value in the pilot signal and the symbol decision error, including: initializing estimated state parameters and an estimated error covariance matrix, and calculating predicted state parameters and a predicted error covariance matrix; updating the predicted state parameters and the predicted error covariance matrix using the pilot observation value in the pilot signal and the symbol decision error, and adjusting the Kalman filter parameters; The calculation of the predicted state parameters and the prediction error covariance matrix includes: obtaining the predicted state parameters according to the state prediction equation; calculating the prediction error covariance matrix according to the estimated error covariance matrix; Calculating the prediction error covariance matrix based on the estimated error covariance matrix includes: determining the difference between the phase observation quantity and the predicted state parameter; estimating the noise covariance matrix online using the sliding window average based on the difference between the phase observation quantity and the predicted state parameter; and determining the prediction error covariance matrix using the estimated noise covariance matrix and the state transition matrix; The phase observation amount includes a feedback phase observation amount and a feedforward phase observation amount, and the calculation of the feedback phase observation amount includes: determining a pilot block length; calculating the feedback phase observation amount using hard decision values ​​having the same number as the pilot block length; and the feedforward phase observation amount is calculated by the pilot; The adjusted Kalman filter parameters are used to perform phase compensation on the non-pilot signal to predict the phase noise at the non-pilot moment.

2. The high-order QAM phase noise suppression method according to claim 1, wherein: The adjusting of Kalman filter parameters includes: ； in, is the Kalman gain vector, is the forecast error covariance matrix, is the observation matrix, is the phase observation noise variance.

3. The high-order QAM phase noise suppression method according to claim 1 or 2, characterized in that: Also includes: determining a state parameter vector of phase noise; Determining a state transfer equation for phase noise and frequency offset based on the state parameter vector, the state transfer matrix, and the state transfer process noise; The state transition process noise obeys a Gaussian distribution based on phase noise variance and frequency offset noise variance.

4. A high-order QAM phase noise suppression device, characterized in that: include: An extraction module is configured to perform timing synchronization, Doppler compensation, and frame synchronization processing on a received signal, identify known pilot symbol blocks inserted at preset intervals, and extract pilot signals and non-pilot signals from the received signal; A feedback module is configured to perform phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error, comprising: calculating a feedback phase observation value after performing phase compensation and hard decision on the non-pilot signal; and determining a feedback observation state parameter error based on the feedback phase observation value and the predicted state parameter as the symbol decision error; An adjustment module is used to adjust Kalman filter parameters using the pilot observation value in the pilot signal and the symbol decision error, including: initializing the estimated state parameters and the estimated error covariance matrix, and calculating the predicted state parameters and the predicted error covariance matrix; using the pilot observation value in the pilot signal and the symbol decision error to perform state updates on the predicted state parameters and the predicted error covariance matrix, and adjusting the Kalman filter parameters; the calculation of the predicted state parameters and the predicted error covariance matrix includes: obtaining the predicted state parameters according to the state prediction equation; calculating the predicted error covariance matrix according to the estimated error covariance matrix; The method of calculating the prediction error covariance matrix based on the estimated error covariance matrix includes: determining the difference between the phase observation quantity and the predicted state parameter; using the difference between the phase observation quantity and the predicted state parameter, online estimating the noise covariance matrix using sliding window averaging; determining the prediction error covariance matrix using the estimated noise covariance matrix and the state transition matrix; the phase observation quantity includes a feedback phase observation quantity and a feedforward phase observation quantity, and calculating the feedback phase observation quantity includes: determining the pilot block length; calculating the feedback phase observation quantity using the same number of hard decision values ​​as the pilot block length; the feedforward phase observation quantity is calculated using the pilot; The compensation module is used to perform phase compensation on the non-pilot signal using the adjusted Kalman filter parameters and predict the phase noise at the non-pilot moment.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the high-order QAM phase noise suppression method according to any one of claims 1 to 3 is implemented.

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