Method and apparatus for phase noise prediction and compensation in high-order satellite communication systems

Abstract

This invention belongs to the field of satellite communication technology and provides a method and apparatus for phase noise prediction and compensation in high-order satellite communication systems. The method includes: acquiring a received signal corresponding to an original signal transmitted by a transmitter; wherein the original signal contains known pilot symbols; determining the theoretical fine-compensation phase and Doppler prediction values ​​of the first two received symbols in the received signal, constructing a feature vector, and performing coarse phase compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein the received symbols include pilot symbols and non-pilot symbols; determining historical phase values ​​and key environmental parameters, inputting the historical phase values ​​and key environmental 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 based on the fine-compensation phase value to obtain a second compensated signal. The solution provided by this invention can effectively suppress phase noise interference and improve the stability and reliability of high-order satellite communication systems.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a method and apparatus for phase noise prediction and compensation in high-order satellite communication systems. Background Technology

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

[0003] For a single-frequency signal source, the ideal signal spectrum should be an infinitely narrow spectral line. However, in practical applications, the signal spectrum is not like this; the measured spectral line has a certain width and is accompanied by periodic spurious interference or random phase shifts, which constitute phase noise. In practical communication systems, phase noise has a wide range of sources. It exists not only in frequency processing modules such as local oscillators, modulators, and demodulators, but also in factors such as the nonlinearity of RF modules, multipath fading and Doppler effects in the channel, and sampling frequency deviations in the system. From a frequency domain perspective, noise in the range far from the carrier frequency mainly consists of the phase noise of the voltage-controlled oscillator, while the range closer to the carrier frequency is the phase noise of the reference signal source.

[0004] Phase noise in the local oscillator signal can severely impact communication system performance, leading to reduced carrier frequency tracking accuracy and a significant increase in the system's bit error rate. With advancements in modulation techniques, modulation orders are continuously increasing, but their tolerance for phase noise is decreasing. Traditional carrier phase tracking loops, when faced with significant phase noise, experience loop processing delays that amplify the noise exponentially. For higher-order modulation schemes, this can easily result in non-convergence or loss of lock, leading to insufficient stability and reliability in high-order satellite communication systems.

[0005] Therefore, with the widespread application of high-order modulation systems, there is an urgent need for a phase noise prediction and compensation scheme that is highly resistant to phase noise and has high reliability. Summary of the Invention

[0006] This invention provides a method and apparatus for phase noise prediction and compensation in high-order satellite communication systems, which addresses the shortcomings of insufficient stability and reliability in high-order satellite communication systems caused by phase noise.

[0007] On one hand, the present invention provides a phase noise prediction and compensation method for high-order satellite communication systems, comprising:

[0008] Obtain the received signal corresponding to the original signal sent by the transmitting end; wherein, the original signal has known pilot symbols inserted;

[0009] The theoretical fine-compensation phase and Doppler prediction value of the first two received symbols in the received signal are determined, and the theoretical fine-compensation phase and Doppler prediction value of the first two received symbols are constructed as a feature vector. The received signal is then coarsely phase-compensated based on the feature vector to obtain a first compensated signal. The received symbols include pilot symbols and non-pilot symbols.

[0010] Determine the 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 the finely compensated phase value;

[0011] Based on the finely compensated phase value, the first compensated signal is subjected to fine phase compensation to obtain the second compensated signal.

[0012] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, the received signal is coarsely phase-compensated based on the eigenvector to obtain a first compensation signal, including:

[0013] The current weighting vector and bias are determined using a sliding least squares estimation algorithm.

[0014] The coarse compensation phase value is obtained by adding the product of the feature vector and the weighted vector to the bias.

[0015] The received signal is coarsely phase compensated based on the coarse compensation phase value to obtain a first compensated signal.

[0016] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, determining historical phase values ​​includes:

[0017] Determine the estimated phase error value of the first compensation signal;

[0018] The phase error estimate is processed by a moving average to obtain a smoothed phase error value;

[0019] The historical phase value is obtained by weighted summation of the estimated phase error value and the smoothed phase error value.

[0020] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, determining the estimated phase error value of the first compensated signal includes:

[0021] In the pilot band, the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal are determined, and the phase error estimate of the first compensation signal is calculated based on the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal.

[0022] In the non-pilot band, the phase error estimate of the first compensation signal is determined by a blind phase estimation algorithm.

[0023] 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 multilayer perceptron module, and a fully connected layer.

[0024] The output terminals of the Long Short-Term Memory Network module and the Multilayer Perceptron module are both connected to the fully connected layer.

[0025] According to the phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention, the historical phase value and the key environmental parameters are input into a pre-constructed phase noise prediction model to obtain a finely compensated phase value, including:

[0026] The historical phase value is input into the long short-term memory network module to obtain the first predicted value output by the long short-term memory network module.

[0027] The key environmental parameters are input into the multilayer perceptron module to obtain the second predicted value;

[0028] The first predicted value and the second predicted value are added together by the fully connected layer to obtain the finely compensated phase value.

[0029] 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:

[0030] Acquire historical phase samples, historical environmental parameter samples, and historical fine-compensation phase samples related to high-order satellite communication systems, and establish a historical sample dataset;

[0031] The phase noise prediction model was trained offline using the historical sample dataset to obtain a pre-trained phase noise prediction model.

[0032] The online residual phase samples after phase fine compensation are obtained during the on-orbit operation of the high-order satellite communication system. The model parameters of the initially trained phase noise prediction model are adjusted using the online residual phase samples to obtain the trained phase noise prediction model.

[0033] 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:

[0034] The residual phase value is determined by pilot estimation;

[0035] Based on the residual phase value, residual phase compensation is performed on the second compensation signal to obtain a compensated received signal.

[0036] The phase noise prediction and compensation method for high-order satellite communication systems provided by the present invention determines the residual phase value through pilot estimation, including:

[0037] Determine the pilot symbols received in the second compensated signal and the pilot symbols transmitted in the original signal;

[0038] The residual phase value is calculated based on the pilot symbols received in the second compensation signal and the pilot symbols transmitted in the original signal.

[0039] On the other hand, the present invention also provides a phase noise prediction and compensation device for a high-order satellite communication system, comprising:

[0040] The acquisition module is used to acquire the received signal corresponding to the original signal sent by the transmitting end; wherein, the original signal has known pilot symbols inserted;

[0041] The coarse compensation module is used 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 phase coarse compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols;

[0042] The phase noise prediction module is used to determine historical phase values ​​and key environmental parameters, and input the historical phase values ​​and key environmental parameters into a pre-built phase noise prediction model to obtain finely compensated phase values.

[0043] The fine compensation module is used to perform phase fine compensation on the first compensation signal based on the fine compensation phase value to obtain the second compensation signal.

[0044] The present invention provides a phase noise prediction and compensation method and apparatus for high-order satellite communication systems. This method constructs a feature vector from the theoretical fine-compensation phase and Doppler prediction values ​​of the first two received symbols, and performs coarse phase compensation on the received signal based on this feature vector to obtain a first compensated signal. It then determines historical phase values ​​and key environmental parameters, inputs these values ​​into a pre-constructed phase noise prediction model to obtain fine-compensation phase values. Finally, it performs fine phase compensation on the first compensated signal based on these fine-compensation phase values ​​to obtain a second compensated signal. Through these coarse and fine phase compensation stages, phase noise prediction and phase compensation can be achieved, effectively suppressing phase noise interference and improving the stability and reliability of high-order satellite communication systems. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the phase noise prediction and compensation method for a high-order satellite communication system provided in an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the implementation principle of the phase noise prediction and compensation method for high-order satellite communication systems provided in this embodiment of the invention.

[0048] Figure 3 This is a schematic diagram of the phase noise prediction model;

[0049] Figure 4 This is the original constellation diagram in the performance simulation scenario;

[0050] Figure 5 It is a receiver constellation diagram that introduces phase noise in a performance simulation scenario;

[0051] Figure 6 It is a receiver constellation diagram that introduces phase noise and residual frequency offset in a performance simulation scenario;

[0052] Figure 7 It is a receiver constellation diagram that incorporates phase noise, residual frequency offset, and white noise in a performance simulation scenario;

[0053] Figure 8 This is the receiver constellation diagram corresponding to the existing noise compensation scheme in the absence of noise;

[0054] Figure 9 This is the receiver constellation diagram corresponding to the improved scheme provided in this embodiment when there is no noise;

[0055] Figure 10 This is a schematic diagram illustrating the phase noise suppression effect of existing noise compensation schemes when there is noise.

[0056] Figure 11 This is a schematic diagram illustrating the phase noise suppression effect of the improved scheme provided in this embodiment when there is noise.

[0057] Figure 12 This is a schematic diagram of the phase noise prediction and compensation device for a high-order satellite communication system provided in an embodiment of the present invention;

[0058] Figure 13 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0060] The following is combined Figures 1 to 13 This invention describes in detail the phase noise prediction and compensation method and apparatus for high-order satellite communication systems provided by embodiments of the present invention.

[0061] Figure 1 This is a flowchart illustrating the phase noise prediction and compensation method for high-order satellite communication systems provided in an embodiment of the present invention.

[0062] like Figure 1 As shown in the embodiment of the present invention, the phase noise prediction and compensation method for high-order satellite communication systems mainly includes the following steps:

[0063] Step 110: Obtain the received signal corresponding to the original signal sent by the transmitter; wherein, the original signal has known pilot symbols inserted.

[0064] The method provided in this embodiment is mainly applied to high-order satellite communication systems, which refer to satellite communication systems that employ advanced technologies such as high-order modulation and efficient coding to achieve higher data transmission rates, better spectral efficiency, and stronger anti-interference capabilities.

[0065] In this embodiment, the transmitting end will insert known pilot symbols at certain intervals, and the pilot symbols will be inserted into the original signal in the form of pilot symbol blocks.

[0066] In some embodiments, the received signal can be preprocessed before subsequent coarse phase compensation. In the preprocessing stage, such as... Figure 2 As shown, preprocessing operations such as timing synchronization and frame synchronization can be performed on the received signal. Figure 2 An exemplary embodiment illustrates the implementation principle of a phase noise prediction and compensation method for high-order satellite communication systems.

[0067] Step 120: 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 as a feature vector, and perform phase coarse compensation on the received signal based on the feature vector to obtain the first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols.

[0068] In this embodiment, the feature vector considered in the phase coarse compensation stage includes the theoretical fine compensation phase and Doppler prediction value of the first two received symbols, as detailed below:

[0069] (1)

[0070] Where f represents the feature vector. Indicates the first k The theoretical fine-compensation phase of the first two received symbols before a certain time point. Indicates the first k Doppler forecast values ​​at various time points, where 'd' stands for the first letter of Doppler. f d This is the Doppler forecast value.

[0071] In practical applications, due to the influence of Doppler (mainly caused by the relative motion between the satellite and the ground), there is a large phase difference in the received signal. At the same time, since the Doppler prediction value also has errors, this embodiment performs coarse phase compensation for the large phase difference.

[0072] Step 130: Determine the historical phase values ​​and key environmental parameters, input the historical phase values ​​and key environmental parameters into the pre-built phase noise prediction model, and obtain the finely compensated phase values.

[0073] In satellite communication systems, the prediction of phase noise needs to consider both temporal correlations (such as the cumulative effect of oscillator phase drift) and environmental dynamics (such as temperature changes). To address this, this embodiment utilizes a phase noise prediction model to obtain precisely compensated phase values ​​using historical phase values ​​and key environmental parameters, providing effective data for precise phase compensation.

[0074] Step 140: Based on the fine compensation phase value, perform phase fine compensation on the first compensation signal to obtain the second compensation signal.

[0075] This embodiment uses the finely compensated phase output by the phase noise prediction model to perform fine phase compensation caused by factors such as phase noise based on the first compensation signal, thereby further improving the phase compensation accuracy.

[0076] In one embodiment, coarse phase compensation is performed on the received signal based on the feature vector to obtain a first compensated signal, specifically including:

[0077] First, the current weighting vector and bias are determined using the moving least squares estimation algorithm.

[0078] This embodiment uses a moving least squares estimation algorithm to iteratively update the weighted vector and bias. In the... k When performing sliding calculations at various time points, a key matrix F can be constructed. k and parameter vector The details are as follows:

[0079] (2)

[0080] (3)

[0081] Among them, F k Indicates the first k The key matrix for sliding calculations at each time point ,..., Indicates the first [number]th ... k The theoretically precise phase compensation of the received symbols prior to a given time point. , ,..., Indicates the first [number]th ... k The time point and previous Doppler forecast values, N Indicates the length of the sliding window. Let w represent the parameter vector, w represent the weighted vector, and b represent the bias. , , This represents the element values ​​in the weighted vector.

[0082] Furthermore, the least squares estimate of the parameter vector is:

[0083] (4)

[0084] in, Represents the parameter vector The least squares estimate, F k Indicates the first k The key matrix for sliding calculations at each time point , ,..., Indicates the first [number]th ...k The time point and the previous coarse compensation phase value.

[0085] Then, the product of the eigenvector and the weighted vector, plus the bias, is added to obtain the coarse compensation phase value.

[0086] This embodiment uses linear prediction to obtain coarse compensation phase values, as detailed below:

[0087] (5)

[0088] in, Indicates the first k The coarse compensation phase value at each time point, where w represents the weighted vector, f represents the eigenvector, and b represents the bias.

[0089] Finally, the received signal is coarsely phase compensated based on the coarse compensation phase value to obtain the first compensated signal.

[0090] In this embodiment, let the first... k The received signal at each time point is x ( k The first compensated signal after phase coarse compensation is as follows:

[0091] (6)

[0092] in, Indicates the first k The first compensation signal at each time point x ( k ) indicates the first k Received signals at each time point Indicates the first k The coarse compensation phase value at each time point, where j represents the imaginary unit.

[0093] In one embodiment, determining the historical phase value specifically includes:

[0094] First, determine the estimated phase error value of the first compensation signal.

[0095] In one specific implementation, determining the phase error estimate of the first compensation signal specifically includes:

[0096] In the pilot band, the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal are determined, and the phase error estimate of the first compensation signal is calculated based on the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal.

[0097] In this embodiment, for the pilot band, the phase error estimate at the current moment can be directly calculated by comparing the phase difference between the received pilot symbol and the transmitted pilot symbol. Specifically, this is implemented using the ML (Maximum Likelihood) algorithm. k The estimated phase error at each time point can be expressed as follows:

[0098] (7)

[0099] in, Indicates the first k Phase error estimates at each time point p pilot symbol pilot symbol The first letter of This indicates the pilot symbol received in the first compensation signal. This represents the pilot symbols transmitted in the original signal. L p Indicates the length of the pilot symbol block.

[0100] In the non-pilot band, the phase error estimate of the first compensation signal is determined by a blind phase estimation algorithm.

[0101] In this embodiment, for the non-pilot 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 estimated value of the phase error of the first compensated signal.

[0102] Then, the phase error estimate is processed by moving average to obtain the smoothed phase error value.

[0103] While the Long Short-Term Memory (LSTM) network module in the phase noise prediction model possesses a certain degree of noise resistance, its prediction performance will significantly degrade if the noise in the input signal exceeds its processing capacity. To prevent significant noise in the phase error estimates, this embodiment applies a moving average to the phase sequence containing the phase error estimates obtained from the blind phase estimation algorithm to suppress high-frequency noise.

[0104] Finally, the estimated phase error and the smoothed phase error are weighted and summed to obtain the historical phase value.

[0105] In this embodiment, the first k The historical phase values ​​at each point in time can be represented as follows:

[0106] (8)

[0107] in, Indicates the first k Historical phase values ​​at each point in time, This represents the smoothed phase error value at the k-th time point. Indicates the first k Weighted step size at each time point Indicates the first k Phase error estimates at each time point.

[0108] In one embodiment, such as Figure 3 As shown, the phase noise prediction model specifically includes: a long short-term memory network module 210, a multilayer perceptron module 220, and a fully connected layer 230.

[0109] The outputs of the Long Short-Term Memory Network module 210 and the Multilayer Perceptron module 220 are both connected to the fully connected layer 230.

[0110] In response to 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 for the phase noise prediction model. The long short term memory network module 210 is used to process time series data and capture long-term dependencies between phases, such as the cumulative effect of oscillator phase drift.

[0111] like Figure 3 As shown, the Long Short-Term Memory (LSTM) network module 210 includes a first input layer 2101, an LSTM layer 2102, and a first output layer 2103. In this embodiment, the LSM network module 210 includes multiple hidden units, and the input is a scalar, namely the historical phase value. The number of hidden units can be obtained through training. Under a certain training error, the number of hidden units should be minimized. During output, the hidden state at the last time step is taken, and the output vector dimension is the number of hidden units.

[0112] like Figure 3 As shown, the multilayer perceptron module 220 is used to process time-independent static environmental features and model the nonlinear relationship between environmental parameters (such as temperature changes and received signal-to-noise ratio) and phase. In this embodiment, the multilayer 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 consists of three nodes, including the temperature of the transmitting oscillator. Receiver oscillator temperature and the signal-to-noise ratio of the received signal Key environmental parameters. This embodiment also designs two hidden layers: a first hidden layer 2202 and a second hidden layer 2203. The number of units in each hidden layer is obtained through training, aiming to minimize the number of hidden units within a certain training error. Both hidden layers use the Rectified Linear Unit (ReLU) activation function, and the vector dimension of the second output layer 2204 is equal to the number of units in the second hidden layer 2203.

[0113] Subsequently, the data output from the Long Short-Term Memory Network module 210 and the Multilayer Perceptron module 220 are concatenated to obtain a joint feature vector with a larger dimension, which is then mapped through a fully connected layer 230 to obtain the finely compensated phase value. .

[0114] In this embodiment, the long short-term memory network module 210 and the multilayer perceptron module 220 are combined through the fully connected layer 230, which can more comprehensively model the dynamic characteristics of phase noise.

[0115] In one embodiment, historical phase values ​​and key environmental parameters are input into a pre-built phase noise prediction model to obtain finely compensated phase values, specifically including:

[0116] On the one hand, the historical phase values ​​are input into the Long Short-Term Memory (LSTM) network module to obtain the first predicted value output by the LTM network module.

[0117] On the other hand, key environmental parameters are input into the multilayer perceptron module to obtain a second predicted value.

[0118] Finally, the first and second predicted values ​​are added together through a fully connected layer to obtain the finely compensated phase value.

[0119] In this embodiment, the first k The precise compensation phase value at each time point can be expressed as follows:

[0120] (9)

[0121] in, Indicates the first k The precise compensation phase value at each time point This represents the historical phase value before the k-th time point. Indicates the temperature of the transmitting oscillator. Indicates the temperature of the receiving oscillator. Indicates the signal-to-noise ratio of the received signal. This represents the first predicted value output by the Long Short-Term Memory (LSTM) network module. This represents the second predicted value output by the multilayer perceptron module.

[0122] In one embodiment, the phase noise prediction model is specifically trained through the following process:

[0123] First, historical phase samples, historical environmental parameter samples, and historical fine-compensation phase samples related to high-order satellite communication systems are obtained to establish a historical sample dataset.

[0124] Then, the phase noise prediction model is trained offline using historical sample datasets to obtain a pre-trained phase noise prediction model.

[0125] Finally, online residual phase samples after phase fine compensation are obtained during the on-orbit operation of the high-order satellite communication system. The model parameters of the initially trained phase noise prediction model are adjusted using the online residual phase samples to obtain the trained phase noise prediction model.

[0126] In this embodiment, the training process of the phase noise prediction model is divided into two stages: the offline pre-training stage and the online training stage.

[0127] The offline pre-training phase is set before the system is delivered and put into operation. It can use historical satellite data, including historical sample datasets related to the received signal under different orbits, different environments (such as temperature), and different received signal power, to conduct preliminary training on the phase noise prediction model.

[0128] In some embodiments, during the offline pre-training stage, artificial noise can be added to the input data to improve the model's noise resistance, thus completing noise injection training.

[0129] The online training phase is set up while the system is in orbit. Pilot symbols are used to calculate online residual phase samples after phase fine-compensation. These samples are then used to fine-tune the initially trained phase noise prediction model online. To prevent online overfitting, this embodiment sets an adaptive step size, as follows:

[0130] (10)

[0131] in, This represents the step size in the k-th training round. Indicates the initial step size. v This represents the attenuation coefficient.

[0132] In practical applications, by selecting appropriate initial step size and decay coefficient, the convergence speed and stability of online training can be balanced.

[0133] This embodiment achieves the initial training of the model through an offline pre-training process to obtain the convergence coefficients. In order to adapt to long-term gradual changes, this embodiment also introduces a dynamic parameter adjustment mechanism. By using online residual phase samples and an adaptive step size, the online fine-tuning of the model parameters is completed, which improves the robustness of the phase noise prediction model.

[0134] The second compensated signal obtained after phase fine compensation can be expressed as:

[0135] (11)

[0136] in, This represents the second compensation signal at the k-th time point. This represents the first compensation signal at the k-th time point. This represents the fine compensation phase value at the k-th time point.

[0137] In one embodiment, after obtaining the second compensation signal, the above method may further include:

[0138] First, the residual phase value is determined by pilot estimation.

[0139] In one specific implementation, the residual phase value is determined through pilot estimation, which specifically includes:

[0140] The first step is to determine the pilot symbols received in the second compensation signal and the pilot symbols transmitted in the original signal.

[0141] The second step is to calculate the residual phase value based on the pilot symbols received in the second compensation signal and the pilot symbols sent in the original signal.

[0142] In this embodiment, pilot symbols are used to estimate the residual phase. It is assumed that the length of the pilot symbol block is... The residual phase value is determined using maximum likelihood estimation at the pilot symbol. The specific calculation formula is as follows:

[0143] (12)

[0144] in, This represents the residual phase value at the k-th time point. z p ( k () represents the pilot symbol received in the second compensation signal at the k-th time point. This represents the pilot symbol transmitted in the original signal at the k-th time point.

[0145] Then, based on the residual phase value, residual phase compensation is performed on the second compensation signal to obtain the compensated received signal.

[0146] In this embodiment, the received signal after compensation is completed can be represented as follows:

[0147] (13)

[0148] in, This represents the received signal at time point k when compensation is complete. This represents the second compensation signal at the k-th time point. This represents the residual phase value at the k-th time point.

[0149] The following examples demonstrate the performance simulation and effect comparison of the phase noise prediction and compensation methods used in high-order satellite communication systems.

[0150] In the performance simulation phase, the simulation parameters can be set as follows:

[0151] Modulation method: 256QAM;

[0152] Symbol rate: 750 Msps;

[0153] Phase noise model: -80dBc / Hz@100Hz, -125dBc / Hz@10MHz;

[0154] Doppler prediction accuracy: 1 kHz;

[0155] Pilot symbol block length: 16 symbols;

[0156] Spacing between pilot symbol blocks: 10,000 symbols;

[0157] Signal-to-noise ratio: Eb / N0 = 24dB.

[0158] Figure 4 , Figure 5 , Figure 6 as well as Figure 7 The original constellation diagram and the receiver constellation diagrams for different types of damage are shown as examples, respectively. Figure 4 The image shown is the original constellation chart. Figure 5 The diagram shown is a receiver constellation diagram with phase noise introduced. Figure 6 The diagram shown is a receiver constellation diagram with phase noise and residual frequency offset introduced. Figure 7 The diagram shows the receiver constellation with reference to phase noise, residual frequency offset, and white noise.

[0159] The phase noise prediction and compensation method for high-order satellite communication systems provided in this embodiment will be compared with existing noise compensation schemes (e.g., patent application CN115580356A, published on January 6, 2023, entitled "A Phase Noise Suppression Method and Device").

[0160] In the absence of noise, the receiver constellation diagram corresponding to the existing noise compensation scheme is as follows: Figure 8 As shown, the residual phase error is 0.83°. The receiving constellation diagram corresponding to the improved scheme provided in this embodiment is as follows. Figure 9 As shown, the residual phase error is 0.4°.

[0161] In the presence of noise, the phase noise suppression effect of existing noise compensation schemes is as follows: Figure 10 As shown, the residual phase error is 1.88°, and the bit error rate is 6.67e-5; the phase noise suppression effect of the improved scheme provided in this embodiment is as follows. Figure 11 As shown, 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 for both are obtained using root mean square (RMS) statistics.

[0162] The comparison shows that, compared with the existing noise compensation scheme, the improved scheme provided in this embodiment reduces the phase error by 52% after phase noise suppression when there is no noise; and improves the bit error rate by two orders of magnitude when there is noise.

[0163] Based on the same general inventive concept, this invention also protects a phase noise prediction and compensation device for high-order satellite communication systems. The phase noise prediction and compensation device for high-order satellite communication systems provided by this invention will be described below. The phase noise prediction and compensation device for high-order satellite communication systems described below can be referred to in correspondence with the phase noise prediction and compensation method for high-order satellite communication systems described above.

[0164] like Figure 12 As shown, the phase noise prediction and compensation device for high-order satellite communication systems provided in this embodiment of the invention specifically includes:

[0165] The acquisition module 310 is used to acquire the received signal corresponding to the original signal sent by the transmitting end; wherein the original signal has known pilot symbols inserted.

[0166] The coarse compensation module 320 is used 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 as a feature vector, and perform phase coarse compensation on the received signal based on the feature vector to obtain the first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols.

[0167] The phase noise prediction module 330 is used to determine historical phase values ​​and key environmental parameters. The historical phase values ​​and key environmental parameters are input into a pre-built phase noise prediction model to obtain finely compensated phase values.

[0168] The fine compensation module 340 is used to perform phase fine compensation on the first compensation signal based on the fine compensation phase value to obtain the second compensation signal.

[0169] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0170] Figure 13This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0171] like Figure 13 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logic instructions in the memory 430 to execute a phase noise prediction and compensation method for a high-order satellite communication system, including: acquiring the received signal corresponding to the original signal transmitted by the transmitter; wherein the original signal has known pilot symbols inserted; 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 as a feature vector, and performing phase coarse compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein the received symbols include pilot symbols and non-pilot symbols; determining historical phase values ​​and key environmental parameters, inputting the historical phase values ​​and key environmental parameters into a pre-constructed phase noise prediction model to obtain a fine-compensation phase value; and performing phase fine compensation on the first compensated signal based on the fine-compensation phase value to obtain a second compensated signal.

[0172] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0173] On the other hand, the present invention also provides a computer program product, which 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 a phase noise prediction and compensation method for a high-order satellite communication system, including: acquiring a received signal corresponding to an original signal transmitted by a transmitter; wherein the original signal has known pilot symbols inserted; 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 phase coarse compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein the received symbols include pilot symbols and non-pilot symbols; determining historical phase values ​​and key environmental parameters, inputting the historical phase values ​​and key environmental parameters into a pre-constructed phase noise prediction model to obtain a fine-compensation phase value; and performing phase fine compensation on the first compensated signal based on the fine-compensation phase value to obtain a second compensated signal.

[0174] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a phase noise prediction and compensation method for a high-order satellite communication system, comprising: acquiring a received signal corresponding to an original signal transmitted by a transmitter; wherein the original signal contains known pilot symbols; determining the theoretical fine-compensation phase and Doppler prediction values ​​of the first two received symbols in the received signal, constructing a feature vector from the theoretical fine-compensation phase and Doppler prediction values ​​of the first two received symbols, and performing coarse phase compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein the received symbols include pilot symbols and non-pilot symbols; determining historical phase values ​​and key environmental parameters, inputting the historical phase values ​​and key environmental 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 based on the fine-compensation phase value to obtain a second compensated signal.

[0175] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0176] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for phase noise prediction and compensation in high-order satellite communication systems, characterized in that, include: Obtain the received signal corresponding to the original signal sent by the transmitting end; wherein, the original signal has known pilot symbols inserted; The theoretical fine-compensation phase and Doppler prediction value of the first two received symbols in the received signal are determined, and the theoretical fine-compensation phase and Doppler prediction value of the first two received symbols are constructed as a feature vector. The received signal is then coarsely phase-compensated based on the feature vector to obtain a first compensated signal. The received symbols include pilot symbols and non-pilot symbols. The phase error estimate of the first compensation signal is determined; the phase error estimate is processed by moving average to obtain a smoothed phase error value; the phase error estimate and the smoothed phase error value are weighted and summed to obtain a historical phase value; key environmental parameters are determined, and the phase noise prediction model includes: a long short-term memory network module, a multilayer perceptron module, and a fully connected layer; the historical phase value is input into the long short-term memory network module to obtain a first predicted value output by the long short-term memory network module; the key environmental parameters are input into the multilayer perceptron module to obtain a second predicted value; the first predicted value and the second predicted value are added through the fully connected layer to obtain a finely compensated phase value. Based on the finely compensated phase value, the first compensated signal is subjected to fine phase compensation to obtain the second compensated signal; The residual phase value is determined by pilot estimation, and residual phase compensation is performed on the second compensation signal based on the residual phase value to obtain the compensated received signal.

2. The phase noise prediction and compensation method for high-order satellite communication systems according to claim 1, characterized in that, Based on the eigenvector, coarse phase compensation is performed on the received signal to obtain a first compensated signal, including: The current weighting vector and bias are determined using a sliding least squares estimation algorithm. The coarse compensation phase value is obtained by adding the product of the feature vector and the weighted vector to the bias. The received signal is coarsely phase compensated based on the coarse compensation phase value to obtain a first compensated signal.

3. The phase noise prediction and compensation method for high-order satellite communication systems according to claim 1, characterized in that, Determining the phase error estimate of the first compensated signal includes: In the pilot band, the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal are determined, and the phase error estimate of the first compensation signal is calculated based on the pilot symbols received in the first compensation signal and the pilot symbols transmitted in the original signal. In the non-pilot band, the phase error estimate of the first compensation signal is determined by a blind phase estimation algorithm.

4. The phase noise prediction and compensation method for high-order satellite communication systems according to claim 1, characterized in that, The output terminals of the Long Short-Term Memory Network module and the Multilayer Perceptron module are both connected to the fully connected layer.

5. The phase noise prediction and compensation method for high-order satellite communication systems according to claim 1, characterized in that, The phase noise prediction model is trained through the following process: Acquire historical phase samples, historical environmental parameter samples, and historical fine-compensation phase samples related to high-order satellite communication systems, and establish a historical sample dataset; The phase noise prediction model was trained offline using the historical sample dataset to obtain a pre-trained phase noise prediction model. The online residual phase samples after phase fine compensation are obtained during the on-orbit operation of the high-order satellite communication system. The model parameters of the initially trained phase noise prediction model are adjusted using the online residual phase samples to obtain the trained phase noise prediction model.

6. The phase noise prediction and compensation method for high-order satellite communication systems according to claim 1, characterized in that, Determining the residual phase value through pilot estimation includes: Determine the pilot symbols received in the second compensated signal and the pilot symbols transmitted in the original signal; The residual phase value is calculated based on the pilot symbols received in the second compensation signal and the pilot symbols transmitted in the original signal.

7. A phase noise prediction and compensation device for high-order satellite communication systems, characterized in that, include: The acquisition module is used to acquire the received signal corresponding to the original signal sent by the transmitting end; wherein, the original signal has known pilot symbols inserted; The coarse compensation module is used 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 phase coarse compensation on the received signal based on the feature vector to obtain a first compensated signal; wherein, the received symbols include pilot symbols and non-pilot symbols; A phase noise prediction module is used to determine the estimated phase error value of the first compensated signal; perform a moving average processing on the estimated phase error value to obtain a smoothed phase error value; perform a weighted summation of the estimated phase error value and the smoothed phase error value to obtain a historical phase value; determine key environmental parameters; the phase noise prediction model includes: a long short-term memory network module, a multilayer perceptron module, and a fully connected layer; input the historical phase value into the long short-term memory network module to obtain a first predicted value output by the long short-term memory network module; input the key environmental parameters into the multilayer perceptron module to obtain a second predicted value; and add the first predicted value and the second predicted value through the fully connected layer to obtain a finely compensated phase value. The fine compensation module is used to perform phase fine compensation on the first compensation signal based on the fine compensation phase value to obtain the second compensation signal; The device is further configured to: determine the residual phase value through pilot estimation, and perform residual phase compensation on the second compensation signal based on the residual phase value to obtain a compensated received signal.

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