High-order QAM phase noise suppression method, device and equipment

Abstract

The invention provides a high-order QAM (Quadrature Amplitude Modulation) phase noise suppression method, device and equipment, and belongs to the technical field of signal transmission, and the method comprises the following steps: carrying out timing synchronization, Doppler compensation and frame synchronization processing on a received signal, then 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 a Kalman filtering parameter by using a pilot frequency observation value and a symbol judgment error in the pilot frequency signal; according to the method, the Kalman filtering parameters are adjusted, phase compensation is carried out on non-pilot signals through the adjusted Kalman filtering parameters, phase noise at the non-pilot moment is predicted, the Kalman filtering parameters are adjusted through pilot observation values and symbol judgment errors, the method can better adapt to time-varying phase noise characteristics, and the suppression capacity of high-order QAM phase noise is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of signal transmission technology, and in particular to a method, device and equipment for suppressing high-order QAM phase noise. Background Art

[0002] The noise in the communication system is mainly divided into additive noise and multiplicative noise. Additive noise is represented by additive Gaussian white noise in the channel, and multiplicative noise is represented by phase noise. From the perspective of the frequency source, phase noise represents the frequency stability of these devices. Among them, long-term stability is mainly caused by slow frequency changes due to temperature and aging; short-term stability refers to random and rapid phase fluctuations or frequency fluctuations.

[0003] For a single-frequency signal source, its ideal state should be that the spectrum is an infinitely narrow spectrum line. In practical applications, the spectrum of any signal is not absolutely pure. The spectrum line that can be measured will have a certain width and will have periodic stray interference or random phase offsets. These interferences or offsets can be called phase noise. In actual systems, phase noise mainly comes from frequency processing related modules, such as local oscillators, modulators, demodulators, frequency dividers, mixers, up and down converters, etc. In addition, other factors, such as the nonlinearity of the RF module, the multipath fading and Doppler effect of the channel, and the sampling frequency deviation in the system, will also introduce phase noise. From the frequency domain analysis, the main part of the phase noise is the noise far away from the carrier frequency, which is mainly the phase noise of the voltage-controlled oscillator; the phase noise of the reference signal source is in the range close to the carrier frequency.

[0004] The phase noise of the local oscillator signal will affect the carrier frequency tracking accuracy and increase the system bit error rate. The higher the modulation order, the worse the tolerance to phase noise. When the phase noise of the traditional carrier phase tracking loop is too large, the loop processing delay This will cause the phase noise to be amplified exponentially (delay Zoom in when shooting times), for high-order modulation systems, it is easy to fail to converge or lose lock.

[0005] Therefore, how to improve the suppression capability of high-order QAM phase noise has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0006] The present invention provides a high-order QAM phase noise suppression method, device and equipment, which are used to solve the defects of poor suppression ability of high-order QAM phase noise in the prior art, and easy failure to converge or loss of lock.

[0007] In a first aspect, the present invention provides a high-order QAM phase noise suppression method, comprising: After performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identify the known pilot symbol blocks inserted at preset intervals, and extract the pilot signals and non-pilot signals from the received signal; Perform phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors; Use the pilot observations in the pilot signals and the symbol decision errors to adjust the Kalman filter parameters; Perform phase compensation on the non-pilot signals using the adjusted Kalman filter parameters to predict the phase noise at non-pilot times.

[0008] According to a high-order QAM phase noise suppression method provided by the present invention, the performing phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors includes: After performing phase compensation and hard decision on the non-pilot signals, calculate the feedback phase observation; Based on the feedback phase observation and the predicted state parameters, determine the feedback observation state parameter error as the symbol decision error.

[0009] According to a high-order QAM phase noise suppression method provided by the present invention, the using the pilot observations in the pilot signals and the symbol decision errors to adjust the Kalman filter parameters includes: Initialize the estimated state parameters and the estimated error covariance matrix, and calculate the predicted state parameters and the predicted error covariance matrix; Use the pilot observations in the pilot signals and the symbol decision errors to perform state update on the predicted state parameters and the predicted error covariance matrix, and adjust the Kalman filter parameters.

[0010] According to a high-order QAM phase noise suppression method provided by the present invention, the calculating the predicted state parameters and the predicted error covariance matrix includes: Obtain the state prediction parameters according to the state prediction equation; Calculate the predicted error covariance matrix according to the estimated error covariance matrix.

[0011] According to a high-order QAM phase noise suppression method provided by the present invention, the calculating the predicted error covariance matrix according to the estimated error covariance matrix includes: Determine the difference between the phase observation and the predicted state parameters; Use the difference between the phase observation and the predicted state parameters to online estimate the noise covariance matrix by means of moving window averaging; Use the estimated noise covariance matrix and the state transition matrix to determine the predicted error covariance matrix.

[0012] A high - order QAM phase noise suppression method provided by the present invention, the adjusting of the Kalman filter parameters includes: ; Wherein, is the Kalman gain vector, is the predicted error covariance matrix, is the observation matrix, is the variance of the phase observation noise.

[0013] A high - order QAM phase noise suppression method provided by the present invention, the calculating of the feedback phase observation includes: Determine the pilot block length; Calculate the feedback phase observation using the same number of hard - decision values as the pilot block length.

[0014] A high - order QAM phase noise suppression method provided by the present invention further includes: Determine the state parameter vector of the phase noise; Based on the state parameter vector, the state transition matrix and the state transition process noise, determine the state transition equation of the phase noise and the frequency offset; Wherein, the state transition process noise follows a Gaussian distribution based on the phase noise variance and the frequency offset noise variance.

[0015] In a second aspect, the present invention provides a high - order QAM phase noise suppression device, including: An extraction module, configured to perform timing synchronization, Doppler compensation and frame synchronization processing on the received signal, identify the known pilot symbol blocks inserted at preset intervals, and extract the pilot signal and the non - pilot signal in the received signal; A feedback module, configured to perform phase compensation and hard - decision on the non - pilot signal to obtain a symbol decision error; An adjustment module, configured to adjust the Kalman filter parameters by using the pilot observations in the pilot signal and the symbol decision error; A compensation module, configured to perform phase compensation on the non - pilot signal by using the adjusted Kalman filter parameters and predict the phase noise at non - pilot times.

[0016] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the high - order QAM phase noise suppression method as described in any one of the above is implemented.

[0017] In a fourth aspect, the present invention further provides a non - transitory computer - readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the high - order QAM phase noise suppression method as described in any one of the above is implemented.

[0018] In a fifth aspect, the present invention further provides a computer program product, including a computer program which, when executed by a processor, implements the high-order QAM phase noise suppression method as described in any one of the above.

[0019] A high-order QAM phase noise suppression method, apparatus and device provided by the present invention. The method includes: after performing timing synchronization, Doppler compensation and frame synchronization processing on a received signal, identifying known pilot symbol blocks inserted at preset intervals, and extracting pilot signals and non-pilot signals in the received signal; performing phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors; using pilot observations in the pilot signals and the symbol decision errors to adjust Kalman filter parameters; using the adjusted Kalman filter parameters to perform phase compensation on the non-pilot signals, predicting phase noise at non-pilot times, and adjusting the Kalman filter parameters through the pilot observations and the symbol decision errors, which can better adapt to the characteristics of time-varying phase noise and effectively improve the suppression ability of high-order QAM phase noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a flowchart of the high-order QAM phase noise suppression method provided in this embodiment; Figure 2 It is a schematic diagram of the principle of the high-order QAM phase noise suppression method provided in this embodiment; Figure 3 It is a flowchart of the adjustment of Kalman filter parameters provided in this embodiment; Figure 4 It is a received signal diagram under different impairments during simulation testing; Figure 5 It is a schematic diagram of the phase noise suppression effect of the simulation result and the prior art without noise; Figure 6 It is a schematic diagram of the phase noise suppression effect of the simulation result and the prior art with noise, Figure 7 It is a schematic diagram of the structure of the high-order QAM phase noise suppression apparatus provided in this embodiment; Figure 8 It is a schematic diagram of the structure of the electronic device provided in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Figure 1 It is a schematic flowchart of the high-order QAM phase noise suppression method provided in this embodiment.

[0024] As Figure 1 shown, the high-order QAM phase noise suppression method provided in the embodiments of the present invention mainly includes the following steps: 101. After performing timing synchronization, Doppler compensation and frame synchronization processing on the received signal, identify the known pilot symbol blocks inserted at preset intervals, and extract the pilot signal and non-pilot signal in the received signal.

[0025] In a specific implementation process, the transmitting end first inserts pilots into the signal. As shown in Table 1, it is the format after the transmitting end inserts known pilot symbol blocks at certain intervals; Table 1

[0026] Known pilot symbol blocks are inserted into the signal transmitted by the transmitting end. Therefore, when the signal is received, it includes the known pilot symbol blocks. After performing timing synchronization, Doppler compensation and frame synchronization processing on the received signal, extract the received signal to obtain the pilot signal and non-pilot signal.

[0027] 102. Perform phase compensation and hard decision on the non-pilot signal to obtain the symbol decision error.

[0028] As Figure 2 shown, it is a schematic diagram of the principle of the high-order QAM phase noise suppression method. After extracting the pilot signal and non-pilot signal, the non-pilot signal enters the phase compensation module and then is feedback-tracked through the hard decision module to obtain the symbol decision error and input it into the Kalman filter module. Specifically, after performing phase compensation and hard decision on the non-pilot signal, calculate the feedback phase observation quantity, and based on the feedback phase observation quantity and the predicted state parameters, determine the feedback observation state parameter error as the symbol decision error.

[0029] 103. Use the pilot observations in the pilot signal and the symbol decision error to adjust the Kalman filter parameters.

[0030] Adjust the Kalman filter parameters through pilot-assisted forward correction and feedback tracking-based reverse correction. Utilize the pilot observations and symbol decision errors to update the phase state simultaneously, and complete the effective adjustment of the Kalman filter parameters.

[0031] Pilot points provide real phase information, which is used to correct the prediction results of the Kalman filter forwardly, improving the accuracy of estimation. The phase noise of non-pilot points needs to be predicted and compensated through the Kalman filter. Meanwhile, through the compensated symbols, the phase error is extracted and used to correct the measurement results of the Kalman filter backwardly. Through the two-level correction before and after, it is ensured that the corrected Kalman filter coefficients can not only improve the phase noise compensation accuracy but also have dynamic tracking performance, reducing the pilot overhead.

[0032] 104. Use the adjusted Kalman filter parameters to perform phase compensation on non-pilot signals and predict the phase noise at non-pilot times.

[0033] After adjusting the Kalman filter parameters through two-level forward and backward adjustments, then use the finally adjusted Kalman filter parameters to perform phase compensation on non-pilot signals, that is, perform phase rotation on the received signal, perform Kalman filter phase tracking, and realize the prediction of the phase noise at non-pilot times.

[0034] Furthermore, this implementation also includes a joint phase-frequency offset state model, which simultaneously tracks phase noise and frequency offset by expanding the state variables, avoiding the accumulation of phase errors caused by the coupling of the two. Specifically, constructing the state space model of the joint phase-frequency offset consists of the following steps: Determine the state parameter vector of phase noise. The dynamic characteristics of phase noise can be modeled as a Wiener process with frequency offset. The state parameter vector of the th symbol is denoted as (1): (1) where represents the phase error (rad) of the th symbol, and represents the residual frequency offset (Hz) of the th symbol.

[0035] Based on the state parameter vector, state transition matrix, and state transition process noise, determine the state transition equations of phase noise and frequency offset, as shown in (2): (2) where represents the state parameter vector of the th symbol, the matrix is the state transition matrix, which is a fixed matrix, specifically (3): (3) Among them, is the symbol period, is the state transition process noise of the -th symbol, which follows a Gaussian distribution based on the phase noise variance and the frequency offset noise variance, that is , and the matrix is a 2×2 noise covariance matrix, expressed as (4): (4) Among them, , are the phase noise variance and the frequency offset noise variance respectively.

[0036] Further, based on the above embodiments, in this embodiment, the Kalman filter parameters are adjusted by using the pilot observations and the symbol decision errors in the pilot signal, including: Initialize the estimated state parameters, as in (5): (5) Initialize the estimated error covariance matrix, as in (6): (6) After completion of the initialization, perform state prediction. According to the state prediction equation, obtain the predicted state parameter of the -th symbol as (7): (7) represents the predicted state parameter of the -th symbol.

[0037] According to the estimated error covariance matrix, calculate the predicted error covariance matrix of the -th symbol as (8): (8) Among them, represents the predicted error covariance matrix of the -1 -th symbol, and the noise covariance matrix of the -th symbol is obtained through adaptive smoothing filtering, that is, the noise covariance matrix is estimated online by moving window averaging to achieve adaptive update of the noise parameters, as shown in the following formula (9): (9) Among them, is the estimated noise covariance matrix of the -th symbol, is the smoothing factor, is the Kalman gain vector, is the observation state parameter error, which is the difference between the phase observation and the predicted state parameter, denoted as (10): (10) where, is the observation matrix, designed to observe only the phase error, i.e., .

[0038] Denote the noisy phase observation as , expressed as (11): (11) where, is the phase observation noise, which follows a Gaussian distribution, i.e., , is the variance of the phase observation noise.

[0039] There are two calculation methods for the noisy phase observation. One is through the pilot calculation, denoted as the feedforward phase observation . Assume the pilot block length is , and the noisy phase observation is estimated by ML (Maximum Likelihood) at the pilot, i.e., (12): (12) where, is the received pilot symbol, is the transmitted pilot symbol, is the pilot block length, is the pilot symbol variable. The variance of the phase observation noise estimated by the formula is: (13) where, is the signal-to-noise ratio, i.e., the ratio of the symbol energy to the noise variance, and the signal-to-noise ratio can be estimated by the minimum mean square error (MMSE) algorithm at the pilot.

[0040] Another calculation method for the noisy phase observation is through the hard decision value calculation, denoted as the feedback phase observation . Similarly, hard decision values are used to calculate the phase observation, such as (14): (14) where, is the received symbol after phase compensation at non-pilot positions, is the hard decision value of the received symbol at non-pilot positions, i.e., the constellation point in the QAM constellation that is closest to the received symbol.

[0041] After the completion status prediction, the status update is performed. The predicted status parameters and the predicted error covariance matrix are updated using the pilot observations and symbol decision errors (i.e., hard decision feedback values) in the pilot signal, and the Kalman filter parameters are adjusted.

[0042] The estimated status parameters at time k are updated as (15): (15) The estimated error covariance matrix is updated as (16): (16) Where, is the Kalman gain vector, and the update process is (17) (17) Where, is the Kalman gain vector, is the predicted error covariance matrix, is the observation matrix, is the variance of the phase observation noise.

[0043] Finally, phase compensation is performed. According to the estimated status parameters at time k, the received signal at non-pilot positions is phase-compensated, i.e., (18) (18) Figure 3 The flowchart of the Kalman filter parameter adjustment provided in this embodiment is shown as follows.

[0044] As Figure 3 shown, after the input signal undergoes frame synchronization processing, the pilot is extracted into a pilot signal and a non-pilot signal. One path of the pilot signal calculates the feedforward phase observation, combines the predicted status parameters, and determines the feedforward observation status parameter error. One path of the non-pilot signal calculates the feedback phase observation after phase compensation and hard decision, combines the predicted status parameters, and determines the feedback observation status parameter error. After processing the feedforward observation status parameter error on the pilot-assisted side and the feedback observation status parameter error on the feedback tracking side to obtain the observation status parameter error, the noise covariance matrix and the predicted error covariance matrix are calculated. At the same time, the predicted error covariance matrix is affected by the estimated error covariance matrix. Finally, after being adjusted by the noise covariance matrix and the estimated error covariance matrix, the predicted error covariance matrix is obtained, the new Kalman gain is calculated, and the output is obtained after phase compensation. At this time, the Kalman gain is also adjusted by the estimated signal-to-noise ratio value in the pilot signal. The adjusted Kalman gain is also affected by the estimated status parameters, the state transition matrix, and the predicted status parameters to ensure the final phase compensation accuracy.

[0045] The adjustment of Kalman filter parameters is achieved through feedforward regulation on the pilot-assisted side and feedback regulation on the feedback tracking side, and then the phase compensation method can estimate the noise covariance matrix and the variance of the phase observation noise in real time, adapting to time-varying phase noise characteristics (such as oscillator aging and temperature drift). This ensures that the high-order QAM system can achieve high-precision phase noise suppression with low pilot overhead, providing core technical support for high-throughput satellite communication.

[0046] To verify the effectiveness of the technical solution of the present invention, the following performance simulations and comparisons were carried out. The simulation parameters are: Modulation method: 128QAM Symbol rate: 750Msps Phase noise model: -80 dBc / Hz@100Hz, -125dBc / Hz@10MHz Residual frequency offset: 10 kHz Pilot block length: 16 symbols Pilot block interval: 5000 symbols Signal-to-noise ratio: Eb / N0 = 20dB The simulation results are as follows: Figure 4 It is the received schematic diagram for different impairments. a is the original constellation diagram, b is the phase noise schematic diagram, c is the schematic diagram of phase noise and residual frequency offset, and d is the schematic diagram of phase noise, residual frequency offset and white noise. It can be seen that due to the influence of phase noise and residual frequency offset, the received signal constellation diagram will rotate, resulting in bit errors.

[0047] Figure 5 It is the schematic diagram of the phase noise suppression effect of the simulation result without noise and the prior art. The residual phase error of the prior art is 0.8° (RMS), and the residual phase error of the technical solution of this application is 0.34° (RMS).

[0048] Figure 6 It is the schematic diagram of the phase noise suppression effect of the simulation result with noise and the prior art. 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 solution of this application is 2.7° (RMS), and the bit error rate is 5.7e-6.

[0049] Through simulation tests, it is obtained that compared with the prior art solution, when there is no noise, the phase error after phase noise suppression is reduced by 57.5%; when there is noise, the BER is improved by one order of magnitude.

[0050] Based on the same general inventive concept, the present invention also protects a high - order QAM phase noise suppression device. The high - order QAM phase noise suppression device described below can be correspondingly referred to in relation to the high - order QAM phase noise suppression method described above.

[0051] Figure 7 It is a schematic structural diagram of the high - order QAM phase noise suppression device provided in this embodiment.

[0052] As Figure 7 shown, a high - order QAM phase noise suppression device provided in this embodiment includes: An extraction module 701, configured to, after performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identify known pilot symbol blocks inserted at a preset interval, and extract pilot signals and non - pilot signals in the received signal; A feedback module 702, configured to perform phase compensation and hard decision on the non - pilot signals to obtain symbol decision errors; An adjustment module 703, configured to adjust Kalman filter parameters by using pilot observations in the pilot signals and the symbol decision errors; A compensation module 704, configured to perform phase compensation on the non - pilot signals by using the adjusted Kalman filter parameters to predict phase noise at non - pilot times.

[0053] Figure 8 It is a schematic structural diagram of the electronic device provided in this embodiment.

[0054] As Figure 8 shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute the high - order QAM phase noise suppression method, which includes: after performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identifying known pilot symbol blocks inserted at a preset interval, and extracting pilot signals and non - pilot signals in the received signal; performing phase compensation and hard decision on the non - pilot signals to obtain symbol decision errors; adjusting Kalman filter parameters by using pilot observations in the pilot signals and the symbol decision errors; performing phase compensation on the non - pilot signals by using the adjusted Kalman filter parameters to predict phase noise at non - pilot times.

[0055] In addition, when the logical instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs, Read-Only Memories), random access memories (RAMs, Random Access Memories), magnetic disks, or optical discs that can store program codes.

[0056] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the high-order QAM phase noise suppression method provided by the above-mentioned various methods. The method includes: after performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting the pilot signal and non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; using the pilot observation value in the pilot signal and the symbol decision error to adjust the Kalman filter parameters; and using the adjusted Kalman filter parameters to perform phase compensation on the non-pilot signal to predict the phase noise at non-pilot times.

[0057] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the high-order QAM phase noise suppression method provided by the above-mentioned various methods. The method includes: after performing timing synchronization, Doppler compensation, and frame synchronization processing on the received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting the pilot signal and non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; using the pilot observation value in the pilot signal and the symbol decision error to adjust the Kalman filter parameters; and using the adjusted Kalman filter parameters to perform phase compensation on the non-pilot signal to predict the phase noise at non-pilot times.

[0058] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0059] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the 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 invention.

Claims

1. A method for suppressing phase noise of high-order QAM, characterized in that Including: 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 signals to obtain symbol decision errors; Using the pilot observations in the pilot signals and the symbol decision errors to adjust the Kalman filter parameters; Performing phase compensation on the non-pilot signals using the adjusted Kalman filter parameters to predict the phase noise at non-pilot times.

2. The high-order QAM phase noise suppression method according to claim 1, wherein The performing phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors includes: After performing phase compensation and hard decision on the non-pilot signals, calculating the feedback phase observation; Based on the feedback phase observation and the predicted state parameters, determining the feedback observation state parameter error as the symbol decision error.

3. The high-order QAM phase noise suppression method according to claim 1, characterized in that The using the pilot observations in the pilot signals and the symbol decision errors to adjust the Kalman filter parameters includes: 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 observations in the pilot signals and the symbol decision errors to perform state update on the predicted state parameters and the predicted error covariance matrix, and adjusting the Kalman filter parameters.

4. The high-order QAM phase noise suppression method according to claim 3, wherein The calculating the predicted state parameters and the predicted error covariance matrix includes: Obtaining the state prediction parameters according to the state prediction equation; Calculating the predicted error covariance matrix according to the estimated error covariance matrix.

5. The high-order QAM phase noise suppression method according to claim 4, wherein The calculating the predicted error covariance matrix according to the estimated error covariance matrix includes: Determining the difference between the phase observation and the predicted state parameters; Using the difference between the phase observation and the predicted state parameters to online estimate the noise covariance matrix by means of sliding window averaging; Using the estimated noise covariance matrix and the state transition matrix to determine the predicted error covariance matrix.

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

7. The high-order QAM phase noise suppression method according to claim 2, wherein The calculating the feedback phase observation includes: Determining the pilot block length; Calculating the feedback phase observation using the same number of hard decision values as the pilot block length.

8. The high-order QAM phase noise suppression method according to any one of claims 1-7, characterized in that Also including: Determining the state parameter vector of the phase noise; Based on the state parameter vector, the state transition matrix, and the state transition process noise, determining the state transition equation of the phase noise and the frequency offset; Wherein, the state transition process noise follows a Gaussian distribution based on the phase noise variance and the frequency offset noise variance.

9. A high-order QAM phase noise suppression device, characterized in that, Including: An extraction module, configured to, after performing timing synchronization, Doppler compensation, and frame synchronization processing on the 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, configured to perform phase compensation and hard decision on the non-pilot signals to obtain symbol decision errors; An adjustment module, configured to use the pilot observations in the pilot signals and the symbol decision errors to adjust the Kalman filter parameters; A compensation module, configured to perform phase compensation on the non-pilot signals using the adjusted Kalman filter parameters to predict the phase noise at non-pilot times.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the high-order QAM phase noise suppression method according to any one of claims 1 to 8.

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