Phase noise prediction and compensation method and device for high-order satellite communication system

Abstract

The invention belongs to the technical field of satellite communication, and provides a phase noise prediction and compensation method and device for a high-order satellite communication system, and the method comprises the steps: obtaining a receiving signal corresponding to an original signal transmitted by a transmitting end; wherein a known pilot symbol is inserted into the original signal; determining a theoretical fine compensation phase and a Doppler forecast value of the first two received symbols in the received signal, constructing a feature vector, and performing phase coarse compensation on the received signal according to the feature vector to obtain a first compensation signal; wherein the receiving symbols comprise pilot frequency symbols and non-pilot frequency symbols; determining a historical phase value and an environment key parameter, and inputting the historical phase value and the environment key parameter into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; and performing phase fine compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal. According to the scheme provided by the invention, phase noise interference can be effectively suppressed, and the stability and reliability of a high-order satellite communication system are improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a phase noise prediction and compensation method and device for a high-order satellite communication system. Background Art

[0002] In modern communication systems, noise has a crucial impact on system performance, and noise is mainly divided into additive noise and multiplicative noise. Additive Gaussian white noise, as a typical representative of additive noise, is widely present in the channel; and the phase noise in multiplicative noise characterizes the frequency stability of the device from the perspective of the frequency source, including the long-term stability reflected by the slow frequency change caused by temperature, aging and other reasons, and the short-term stability corresponding to the random and rapid phase or frequency fluctuations.

[0003] For a single-frequency signal source, the signal spectrum should ideally be an infinitely narrow spectrum line, but in actual applications, the signal spectrum is not like this. The spectrum line actually measured has a certain width and is accompanied by periodic stray interference or random phase offsets, which are phase noise. In actual communication systems, phase noise comes from a wide range of sources. It not only exists in frequency processing related modules such as local oscillators, modulators, and demodulators, but also in the nonlinearity of RF modules, multipath fading and Doppler effects of channels, and sampling frequency deviations in the system. Factors such as these will also introduce phase noise. From a frequency domain analysis, the noise far from the carrier frequency is mainly composed of the phase noise of the voltage-controlled oscillator, while the noise close to the carrier frequency is the phase noise of the reference signal source.

[0004] The phase noise of the local oscillator signal will seriously affect the performance of the communication system, resulting in reduced carrier frequency tracking accuracy and a significant increase in the system bit error rate. With the development of modulation technology, the modulation order continues to increase, but its tolerance for phase noise is getting worse and worse. When facing large phase noise, the traditional carrier phase tracking loop will multiply the phase noise due to the loop processing delay. For high-order modulation systems, it is very easy to fail to converge or lose lock, resulting in insufficient stability and reliability of high-order satellite communication systems.

[0005] Therefore, with the widespread application of high-order modulation systems, a phase noise prediction and compensation solution with strong anti-phase noise capability and high reliability is urgently needed. Summary of the invention

[0006] The present invention provides a phase noise prediction and compensation method and device for a high-order satellite communication system, which are used to solve the defect that the high-order satellite communication system has insufficient stability and reliability due to the existence of phase noise.

[0007] In one aspect, the present invention provides a phase noise prediction and compensation method for a high-order satellite communication system, comprising: Obtain a received signal corresponding to the original signal sent by the transmitting end; wherein, known pilot symbols are inserted into the original signal. Determine the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, construct the theoretical fine compensation phase and Doppler prediction value of the first two received symbols into a feature vector, and perform coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal; wherein, the received symbols include pilot symbols and non-pilot symbols. Determine the historical phase value and environmental key parameters, input the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value. Perform fine phase compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal.

[0008] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal includes: Determine the current weighted vector and bias through a sliding least squares estimation algorithm. Add the product of the feature vector and the weighted vector and the bias to obtain a coarse compensation phase value. Perform coarse phase compensation on the received signal according to the coarse compensation phase value to obtain a first compensation signal.

[0009] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, determining the historical phase value includes: Determine the phase error estimation value of the first compensation signal. Perform a sliding average process on the phase error estimation value to obtain a phase error smoothing value. Perform a weighted summation on the phase error estimation value and the phase error smoothing value to obtain the historical phase value.

[0010] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, determining the phase error estimation value of the first compensation signal includes: In the pilot frequency band, determine the received pilot symbols in the first compensation signal and the transmitted pilot symbols in the original signal, and calculate the phase error estimation value of the first compensation signal according to the received pilot symbols in the first compensation signal and the transmitted pilot symbols in the original signal. In the non-pilot frequency band, determine the phase error estimation value of the first compensation signal through a blind phase estimation algorithm.

[0011] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, the phase noise prediction model includes: a long short-term memory network module, a multi-layer perceptron module, and a fully connected layer; The output ends of the long short-term memory network module and the multi-layer perceptron module are both connected to the fully connected layer.

[0012] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, inputting the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a refined compensation phase value includes: Inputting the historical phase value into the long short-term memory network module to obtain a first prediction value output by the long short-term memory network module; Inputting the environmental key parameters into the multi-layer perceptron module to obtain a second prediction value; Adding the first prediction value and the second prediction value through the fully connected layer to obtain a refined compensation phase value.

[0013] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, the phase noise prediction model is trained through the following process: Obtain historical phase samples, historical environmental parameter samples, and historical refined compensation phase samples related to high-order satellite communication systems, and establish a historical sample data set; Perform offline training on the phase noise prediction model through the historical sample data set to obtain a preliminarily trained phase noise prediction model; Obtain online residual phase samples after phase refined compensation during the on-orbit operation of the high-order satellite communication system, and adjust the model parameters of the preliminarily trained phase noise prediction model through the online residual phase samples to obtain a trained phase noise prediction model.

[0014] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, after obtaining the second compensation signal, the method further includes: Determine the residual phase value through pilot estimation; Perform residual phase compensation on the second compensation signal based on the residual phase value to obtain a compensated received signal.

[0015] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, determining the residual phase value through pilot estimation includes: Determine the received pilot symbol in the second compensation signal and the transmitted pilot symbol in the original signal; Calculate the residual phase value based on the received pilot symbol in the second compensation signal and the transmitted pilot symbol in the original signal.

[0016] On the other hand, the present invention also provides a phase noise prediction and compensation device for a high-order satellite communication system, including: An acquisition module, configured to acquire a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal; A coarse compensation module, configured to determine the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, construct the theoretical fine compensation phase and Doppler prediction value of the first two received symbols into a feature vector, and perform coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal; wherein, the received symbols include pilot symbols and non-pilot symbols; A phase noise prediction module, configured to determine a historical phase value and environmental key parameters, and input the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; A fine compensation module, configured to perform fine phase compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal.

[0017] The phase noise prediction and compensation method and device for a high-order satellite communication system provided by the present invention construct the theoretical fine compensation phase and Doppler prediction value of the first two received symbols into a feature vector, and perform coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal; determine a historical phase value and environmental key parameters, input the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; perform fine phase compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal. Through the coarse phase compensation link and the fine phase compensation link, phase noise prediction and phase compensation can be realized, thereby effectively suppressing phase noise interference and improving the stability and reliability of the high-order satellite communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 is a schematic flowchart of a phase noise prediction and compensation method for a high-order satellite communication system provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the implementation principle of a phase noise prediction and compensation method for a high-order satellite communication system provided by an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of a phase noise prediction model; Figure 4 is the original constellation diagram in the performance simulation scenario; Figure 5 is the received constellation diagram with phase noise introduced in the performance simulation scenario; Figure 6 is the received constellation diagram with phase noise and residual frequency offset introduced in the performance simulation scenario; Figure 7 is the received constellation diagram with phase noise, residual frequency offset, and white noise introduced in the performance simulation scenario; Figure 8 is the received constellation diagram corresponding to the existing noise compensation scheme without noise; Figure 9 is the received constellation diagram corresponding to the improved scheme provided in this embodiment without noise; Figure 10 is the schematic diagram of the phase noise suppression effect corresponding to the existing noise compensation scheme with noise; Figure 11 is the schematic diagram of the phase noise suppression effect corresponding to the improved scheme provided in this embodiment with noise; Figure 12 is the schematic diagram of the structure of the phase noise prediction and compensation device for high-order satellite communication systems provided in the embodiments of the present invention; Figure 13 is the schematic diagram of the structure of the electronic device provided in the embodiments of the present invention. Detailed implementation manners

[0020] 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. Obviously, 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 making creative efforts shall fall within the protection scope of the present invention.

[0021] The following combines Figures 1 to 13 to describe the detailed solutions of the phase noise prediction and compensation method and device for high-order satellite communication systems provided in the embodiments of the present invention.

[0022] Figure 1 is the flowchart of the phase noise prediction and compensation method for high-order satellite communication systems provided in the embodiments of the present invention.

[0023] As Figure 1 shown, the phase noise prediction and compensation method for high-order satellite communication systems provided in the embodiments of the present invention mainly includes the following steps: Step 110: Obtain the received signal corresponding to the original signal sent by the sending end; wherein, known pilot symbols are inserted into the original signal.

[0024] The method provided in this embodiment is mainly applied to a high-order satellite communication system, which refers to a satellite communication system that adopts advanced technologies such as high-order modulation and efficient coding to achieve higher data transmission rates, better spectral efficiency, and stronger anti-interference capabilities.

[0025] In this embodiment, the transmitting end will insert known pilot symbols at a certain interval, and the pilot symbols will be inserted into the original signal in the form of a pilot symbol block.

[0026] In some embodiments, before subsequent coarse phase compensation, the received signal can be preprocessed. In the preprocessing step, as Figure 2 shown, preprocessing operations such as timing synchronization and frame synchronization can be performed on the received signal. Figure 2 Exemplarily shows the implementation principle of the phase noise prediction and compensation method for a high-order satellite communication system.

[0027] Step 120: Determine the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, construct a feature vector from the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, and perform coarse phase compensation on the received signal based on the feature vector to obtain a first compensated signal; where the received symbols include pilot symbols and non-pilot symbols.

[0028] In this embodiment, the feature vector considered in the coarse phase compensation step includes the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, specifically as follows: (1) where f represents the feature vector, represents the theoretical fine compensation phase of the first two received symbols before the k th time point, represents the Doppler prediction value at the k th time point, d is the first letter of Doppler, f d which is the Doppler prediction value.

[0029] In practical applications, due to the influence of Doppler (mainly caused by the relative motion between the satellite end and the ground end), there is a large phase difference in the received signal. At the same time, since there is also an error in the Doppler prediction value, this embodiment performs coarse phase compensation on the large phase difference.

[0030] Step 130: Determine the historical phase value and environmental key parameters, and input the historical phase value and environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value.

[0031] In a satellite communication system, the prediction of phase noise needs to consider both timing correlation (such as the cumulative effect of oscillator phase drift) and environmental dynamics (such as temperature changes). In this regard, in this embodiment, a phase noise prediction model uses historical phase values and key environmental parameters to achieve the acquisition of accurately compensated phases, providing effective data basis for accurate phase compensation.

[0032] Step 140: According to the accurately compensated phase value, perform accurate phase compensation on the first compensation signal to obtain a second compensation signal.

[0033] In this embodiment, based on the accurately compensated phase output by the phase noise prediction model, accurate phase compensation caused by factors such as phase noise is completed on the basis of the first compensation signal, thereby further improving the phase compensation accuracy.

[0034] In one embodiment, performing rough phase compensation on the received signal according to the eigenvector to obtain a first compensation signal specifically includes: First, determine the current weighted vector and bias through a sliding least squares estimation algorithm.

[0035] In this embodiment, a sliding least squares estimation algorithm is used to realize the iterative update of the weighted vector and the bias. When performing sliding calculation at the k th time point, a key matrix F k and a parameter vector can be constructed, specifically as follows: (2) (3) Among them, F k represents the key matrix when performing sliding calculation at the k th time point, ,..., represents the theoretical accurately compensated phase of the received symbol before the k th time point within the sliding window, , ,..., represents the Doppler prediction values at and before the k th time point within the sliding window, N represents the length of the sliding window, represents the parameter vector, w represents the weighted vector, b represents the bias, , , represent the element values in the weighted vector.

[0036] Furthermore, the least squares estimated value of the parameter vector is: (4) Among them, represents the parameter vector The least squares estimated value, F k denotes the k key matrix during the sliding calculation at the -th time point, , denotes the k -th time point within the sliding window and the previous coarse compensation phase value.

[0037] Then, add the product of the eigenvector and the weighted vector to the bias to obtain the coarse compensation phase value.

[0038] In this embodiment, linear prediction is adopted to obtain the coarse compensation phase value, specifically as follows: (5) wherein, denotes the k -th time point's coarse compensation phase value, w denotes the weighted vector, f denotes the eigenvector, and b denotes the bias.

[0039] Finally, perform coarse phase compensation on the received signal based on the coarse compensation phase value to obtain the first compensation signal.

[0040] In this embodiment, let the received signal at the k -th time point be x ( k ), and the first compensation signal after coarse phase compensation is as follows: (6) wherein, denotes the k -th time point's first compensation signal, x ( k ) denotes the k -th time point's received signal, denotes the k -th time point's coarse compensation phase value, and j denotes the imaginary unit.

[0041] In one embodiment, determining the historical phase value specifically includes: First, determine the phase error estimated value of the first compensation signal.

[0042] In a specific implementation, determining the phase error estimated value of the first compensation signal specifically includes: In the pilot frequency band, determine the pilot symbol received in the first compensation signal and the pilot symbol transmitted in the original signal, and calculate the phase error estimated value of the first compensation signal based on the pilot symbol received in the first compensation signal and the pilot symbol transmitted in the original signal.

[0043] In this embodiment, for the pilot frequency band, the phase error estimation value at the current moment can be directly calculated by comparing the phase difference between the received pilot symbol and the transmitted pilot symbol, specifically implemented by using the ML (Maximum Likelihood) algorithm. Specifically, the phase error estimation value at the k th time point can be expressed as follows: (7) where represents the phase error estimation value at the k th time point, p is the first letter of the pilot symbol pilot symbol , represents the received pilot symbol in the first compensation signal, represents the transmitted pilot symbol in the original signal, L p represents the length of the pilot symbol block.

[0044] In the non-pilot frequency band, the phase error estimation value of the first compensation signal is determined by a blind phase estimation algorithm.

[0045] In this embodiment, for the non-pilot frequency band, a blind phase estimation algorithm such as the Viterbi-Viterbi (V&V) algorithm or the BPS (Blind Phase Search) algorithm can be used to extract the phase error and obtain the phase error estimation value of the first compensation signal.

[0046] Then, the phase error estimation value is processed by moving average to obtain the phase error smoothing value.

[0047] Considering that although the long short-term memory network module in the phase noise prediction model itself has a certain anti-noise ability, when the noise of the input signal is too large and exceeds the processing ability of the long short-term memory network module, the prediction performance will decrease significantly. To prevent large noise in the phase error estimation value, in this embodiment, the phase sequence containing the phase error estimation value obtained by the blind phase estimation algorithm is processed by moving average to suppress high-frequency noise.

[0048] Finally, the phase error estimation value and the phase error smoothing value are weighted and summed to obtain the historical phase value.

[0049] In this embodiment, the historical phase value at the k th time point can be expressed as follows: (8) where represents the historical phase value at the k th time point, represents the smoothed value of the phase error at the k-th time point, represents the k weighted step size at the represents the k estimated value of the phase error at the

[0050] In one embodiment, as Figure 3 shown, the phase noise prediction model specifically includes: a long short-term memory network module 210, a multi-layer perceptron module 220, and a fully connected layer 230.

[0051] The output ends of the long short-term memory network module 210 and the multi-layer perceptron module 220 are both connected to the fully connected layer 230.

[0052] For the phase characteristics of high-order satellite communication systems, this embodiment designs a hybrid neural network of LSTM (Long Short Term Memory) and MLP (Multi Layer Perceptron) as the network architecture of the phase noise prediction model. Among them, the long short-term memory network module 210 is used to process time series data and capture the long-term dependence between phases, such as the cumulative effect of oscillator phase drift.

[0053] As Figure 3 shown, the long short-term memory network module 210 includes a first input layer 2101, an LSTM layer 2102, and a first output layer 2103. In this embodiment, the long short-term memory network module 210 includes multiple hidden units, and the input is a scalar, that is, the historical phase value . The number of hidden units can be obtained through training. Under a certain training error, the number of hidden units is reduced as much as possible. When outputting, the hidden state of the last time step is taken, and the output vector dimension is the number of hidden units.

[0054] As Figure 3 shown, the multi-layer perceptron module 220 is used to process static environment features that are not related to time and model the non-linear relationship between environmental parameters (such as temperature changes, received signal signal-to-noise ratio) and phases. In this embodiment, the multi-layer perceptron module 220 includes a second input layer 2201, a first hidden layer 2202, a second hidden layer 2203, and a second output layer 2204. The input has 3 nodes, that is, it includes the temperature of the transmitting oscillator , the temperature of the receiving oscillator , and the received signal signal-to-noise ratio Environmental key parameters. At the same time, this embodiment designs two hidden layers, namely the first hidden layer 2202 and the second hidden layer 2203. The number of units in each hidden layer is obtained through training. Under a certain training error, the number of hidden units is reduced as much as possible. The two hidden layers adopt the rectified linear unit (ReLU) activation function, and the vector dimension of the second output layer 2204 is the number of units in the second hidden layer 2203.

[0055] Subsequently, by splicing the data output by the long short-term memory network module 210 and the multi-layer perceptron module 220, a joint feature vector with a larger dimension is obtained, and then the fine compensation phase value is mapped through a fully connected layer 230. 。

[0056] In this embodiment, by combining the long short-term memory network module 210 and the multi-layer perceptron module 220 through the fully connected layer 230, the dynamic characteristics of phase noise can be modeled more comprehensively.

[0057] In one embodiment, inputting the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain the fine compensation phase value specifically includes: On the one hand, input the historical phase value into the long short-term memory network module to obtain the first prediction value output by the long short-term memory network module.

[0058] On the other hand, input the environmental key parameters into the multi-layer perceptron module to obtain the second prediction value.

[0059] Finally, add the first prediction value and the second prediction value through the fully connected layer to obtain the fine compensation phase value.

[0060] In this embodiment, the k fine compensation phase value at the nth time point can be expressed as follows: (9) where, represents the fine compensation phase value at the k nth time point, represents the historical phase value before the kth time point, represents the temperature of the transmitting oscillator, represents the temperature of the receiving oscillator, represents the signal-to-noise ratio of the received signal, represents the first prediction value output by the long short-term memory network module, represents the second prediction value output by the multi-layer perceptron module.

[0061] In one embodiment, the phase noise prediction model is specifically trained through the following process: First, obtain historical phase samples, historical environmental parameter samples, and historical fine compensation phase samples related to the high-order satellite communication system, and establish a historical sample dataset.

[0062] Then, perform offline training on the phase noise prediction model through the historical sample dataset to obtain a preliminarily trained phase noise prediction model.

[0063] Finally, obtain online residual phase samples after phase fine compensation during the on-orbit operation of the high-order satellite communication system, and adjust the model parameters of the preliminarily trained phase noise prediction model through the online residual phase samples to obtain a trained phase noise prediction model.

[0064] In this embodiment, the training process of the phase noise prediction model is divided into two stages, namely the offline pre-training stage and the online training stage.

[0065] The offline pre-training stage is set before the system is delivered for operation. Satellite historical data can be used, including historical sample datasets related to received signals under different orbits, different environments (such as temperature), and different received signal powers, to perform preliminary training on the phase noise prediction model.

[0066] In some embodiments, in the offline pre-training stage, in order to improve the noise resistance of the model, artificial noise can be added to the input data to complete noise injection training.

[0067] The online training stage is set when the system is in on-orbit operation. Pilot symbols can be used to calculate online residual phase samples after phase fine compensation, and the online residual phase samples are used to perform online fine-tuning on the preliminarily trained phase noise prediction model. To prevent online overfitting, this embodiment sets an adaptive step size, specifically as follows: (10) Among them, represents the step size at the k-th training round, represents the initial step size, v represents the attenuation coefficient.

[0068] In practical applications, by selecting appropriate initial step size and attenuation coefficient, the convergence speed and stability of online training can be balanced.

[0069] In this embodiment, the preliminary training of the model is realized through the offline pre-training link to obtain the convergence coefficient; in order to adapt to long-term slow changes, this embodiment also introduces a dynamic parameter adjustment mechanism. Using the online residual phase samples and adopting an adaptive step size, the online fine-tuning of the model parameters is completed, improving the robustness of the phase noise prediction model.

[0070] The second compensation signal obtained after phase fine compensation can be expressed as: (11) Among them, represents the second compensation signal at the k-th time point, represents the first compensation signal at the k-th time point, represents the fine compensation phase value at the k-th time point.

[0071] In one embodiment, after obtaining the second compensation signal, the above method may further include: First, determine the residual phase value through pilot estimation.

[0072] In a specific implementation, determining the residual phase value through pilot estimation specifically includes: The first step is to determine the received pilot symbol in the second compensation signal and the transmitted pilot symbol in the original signal.

[0073] The second step is to calculate the residual phase value based on the received pilot symbol in the second compensation signal and the transmitted pilot symbol in the original signal.

[0074] In this embodiment, pilot symbols are used to estimate the residual phase. Assuming the length of the pilot symbol block is , the maximum likelihood estimation is used to determine the residual phase value at the pilot symbol. The specific calculation formula is as follows: (12) Among them, represents the residual phase value at the k-th time point, z p ( k ) represents the received pilot symbol in the second compensation signal at the k-th time point, represents the transmitted pilot symbol in the original signal at the k-th time point.

[0075] Then, based on the residual phase value, perform residual phase compensation on the second compensation signal to obtain the compensated received signal.

[0076] In this embodiment, the compensated received signal can be expressed as follows: (13) Among them, represents the compensated received signal at the k-th time point, represents the second compensation signal at the k-th time point, represents the residual phase value at the k-th time point.

[0077] Next, the above phase noise prediction and compensation method for high-order satellite communication systems is subjected to performance simulation and effect comparison through specific examples.

[0078] In the performance simulation session, the simulation parameters can be set as follows: Modulation method: 256QAM; Symbol rate: 750Msps; Phase noise model: -80dBc / Hz@100Hz, -125dBc / Hz@10MHz; Doppler prediction accuracy: 1kHz; Length of pilot symbol block: 16 symbols; Interval between pilot symbol blocks: 10000 symbols; Signal-to-noise ratio: Eb / N0 = 24dB.

[0079] Figure 4 and Figure 5 and Figure 6 as well as Figure 7 respectively exemplarily show the original constellation diagram and the received constellation diagrams under different impairments. Among them, Figure 4 is shown as the original constellation diagram, Figure 5 is shown as the received constellation diagram with introduced phase noise, Figure 6 is shown as the received constellation diagram with introduced phase noise and residual frequency offset, Figure 7 is shown as the received constellation diagram with introduced phase noise, residual frequency offset and white noise.

[0080] Subsequently, the above-mentioned phase noise prediction and compensation method for high-order satellite communication systems provided in this embodiment is compared with the existing noise compensation scheme (for example, the invention patent application with the application publication number CN115580356A, the publication date of January 6, 2023, and the invention name of a phase noise suppression method and device).

[0081] When there is no noise, the received constellation diagram corresponding to the existing noise compensation scheme is as shown in Figure 8 , the residual phase error is 0.83°, and the received constellation diagram corresponding to the improved scheme provided in this embodiment is as shown in Figure 9 , the residual phase error is 0.4°.

[0082] When there is noise, the phase noise suppression effect corresponding to the existing noise compensation scheme is as shown in Figure 10 , the residual phase error is 1.88°, and the bit error rate is 6.67e-5; the phase noise suppression effect corresponding to the improved scheme provided in this embodiment is as shown in Figure 11 , the residual phase error is 1.72°, and the bit error rate is 8.33e-7. It should be noted that the residual phase errors of both are obtained by root mean square statistics.

[0083] Through comparison, it can be seen that compared with the existing noise compensation scheme, the improved scheme provided in this embodiment has a 52% reduction in phase error after phase noise suppression when there is no noise; when there is noise, the bit error rate is improved by 2 orders of magnitude.

[0084] Based on the same general inventive concept, the present invention also protects a phase noise prediction and compensation device for a high-order satellite communication system. The phase noise prediction and compensation device for a high-order satellite communication system provided by the present invention will be described below. The phase noise prediction and compensation device for a high-order satellite communication system described below can be correspondingly referred to the phase noise prediction and compensation method for a high-order satellite communication system described above.

[0085] As Figure 12 shown, the phase noise prediction and compensation device for a high-order satellite communication system provided by an embodiment of the present invention specifically includes: An acquisition module 310, configured to acquire a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal.

[0086] A coarse compensation module 320, configured to determine the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, construct a feature vector from the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, and perform coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal; wherein, the received symbols include pilot symbols and non-pilot symbols.

[0087] A phase noise prediction module 330, configured to determine a historical phase value and key environmental parameters, and input the historical phase value and key environmental parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value.

[0088] A fine compensation module 340, configured to perform fine phase compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal.

[0089] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0090] Figure 13 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0091] As Figure 13As shown in the figure, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 complete communication with each other through the communication bus 440. The processor 410 may call logic instructions in the memory 430 to execute a phase noise prediction and compensation method for a high-order satellite communication system, including: obtaining a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal; determining a theoretical fine compensation phase and a Doppler prediction value of the first two received symbols in the received signal, constructing a feature vector from the theoretical fine compensation phase and the Doppler prediction value of the first two received symbols, and performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols; determining a historical phase value and environmental key parameters, inputting the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; and performing fine phase compensation on the first compensated signal according to the fine compensation phase value to obtain a second compensated signal.

[0092] In addition, when the logic instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as independent products, they may 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 the technical solution, may be embodied in the form of a software product. The 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 a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0093] On the other hand, the present invention 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. When the computer program is executed by a processor, the computer can execute a phase noise prediction and compensation method for a high-order satellite communication system, including: obtaining a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal; determining the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, constructing a feature vector from the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, and performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols; determining a historical phase value and environmental key parameters, inputting the historical phase value and environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; and performing fine phase compensation on the first compensated signal according to the fine compensation phase value to obtain a second compensated signal.

[0094] In 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 implements a phase noise prediction and compensation method for a high-order satellite communication system, including: obtaining a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal; determining the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, constructing a feature vector from the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, and performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols; determining a historical phase value and environmental key parameters, inputting the historical phase value and environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; and performing fine phase compensation on the first compensated signal according to the fine compensation phase value to obtain a second compensated signal.

[0095] 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 may be 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. Those of ordinary skill in the art can understand and implement it without creative labor.

[0096] 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 this 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. This 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 to enable 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.

[0097] 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 described 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 phase noise prediction and compensation method for high-order satellite communication systems, characterized in that Including: Obtain a received signal corresponding to an original signal sent by a transmitting end; wherein, known pilot symbols are inserted into the original signal; Determine the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, construct a feature vector from the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, and perform coarse phase compensation on the received signal according to the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols; Determine a historical phase value and environmental key parameters, and input the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; Perform fine phase compensation on the first compensated signal according to the fine compensation phase value to obtain a second compensated signal.

2. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 1, characterized in that, Performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensated signal, including: Determine the current weighted vector and bias through a sliding least squares estimation algorithm; Add the product of the feature vector and the weighted vector and the bias to obtain a coarse compensation phase value; Perform coarse phase compensation on the received signal according to the coarse compensation phase value to obtain a first compensated signal.

3. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 1, characterized in that Determine a historical phase value, including: Determine the phase error estimation value of the first compensated signal; Perform a sliding average process on the phase error estimation value to obtain a phase error smoothing value; Perform a weighted sum on the phase error estimation value and the phase error smoothing value to obtain a historical phase value.

4. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 3, wherein Determine the phase error estimation value of the first compensated signal, including: In the pilot frequency band, determine the received pilot symbols in the first compensated signal and the transmitted pilot symbols in the original signal, and calculate the phase error estimation value of the first compensated signal according to the received pilot symbols in the first compensated signal and the transmitted pilot symbols in the original signal; In the non-pilot frequency band, determine the phase error estimation value of the first compensated signal through a blind phase estimation algorithm.

5. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 1, characterized in that The phase noise prediction model includes: a long short-term memory network module, a multi-layer perceptron module, and a fully connected layer; The output ends of the long short-term memory network module and the multi-layer perceptron module are both connected to the fully connected layer.

6. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 5, characterized in that Inputting the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value, including: Input the historical phase value into the long short-term memory network module to obtain a first prediction value output by the long short-term memory network module; Input the environmental key parameters into the multi-layer perceptron module to obtain a second prediction value; Add the first prediction value and the second prediction value through the fully connected layer to obtain a fine compensation phase value.

7. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 1, wherein The phase noise prediction model is trained through the following process: Obtain historical phase samples, historical environmental parameter samples, and historical fine compensation phase samples related to a high-order satellite communication system, and establish a historical sample data set; Perform offline training on the phase noise prediction model through the historical sample data set to obtain a preliminarily trained phase noise prediction model; Obtain the online residual phase samples after phase fine compensation during the on-orbit operation of a high-order satellite communication system, and adjust the model parameters of the initially trained phase noise prediction model through the online residual phase samples to obtain a trained phase noise prediction model.

8. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 1, wherein After obtaining the second compensation signal, the method further includes: Determine the residual phase value through pilot estimation; According to the residual phase value, perform residual phase compensation on the second compensation signal to obtain a compensated received signal.

9. The phase noise prediction and compensation method for a high-order satellite communication system according to claim 8, characterized in that, Determine the residual phase value through pilot estimation, including: Determine the received pilot symbols in the second compensation signal and the transmitted pilot symbols in the original signal; Calculate the residual phase value according to the received pilot symbols in the second compensation signal and the transmitted pilot symbols in the original signal.

10. A phase noise prediction and compensation device for a high-order satellite communication system, characterized in that, Include: An acquisition module for acquiring the received signal corresponding to the original signal sent by the transmitter; wherein, known pilot symbols are inserted into the original signal; A coarse compensation module for determining the theoretical fine compensation phase and Doppler prediction value of the first two received symbols in the received signal, constructing the theoretical fine compensation phase and Doppler prediction value of the first two received symbols into a feature vector, and performing coarse phase compensation on the received signal according to the feature vector to obtain a first compensation signal; wherein, the received symbols include pilot symbols and non-pilot symbols; A phase noise prediction module for determining the historical phase value and environmental key parameters, and inputting the historical phase value and the environmental key parameters into a pre-constructed phase noise prediction model to obtain a fine compensation phase value; A fine compensation module for performing fine phase compensation on the first compensation signal according to the fine compensation phase value to obtain a second compensation signal.

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