Channel compensation method and device, electronic equipment and storage medium

Abstract

The invention discloses a channel compensation method and device, electronic equipment and a storage medium, and relates to the technical field of signal processing. The method comprises the following steps: acquiring a receiving end pilot signal and a transmitting end pilot signal; performing signal estimation on the receiving end pilot signal and the transmitting end pilot signal by adopting an initial channel estimation algorithm to obtain an initial channel compensation matrix; based on a pre-trained channel compensation matrix updating model, correcting the initial channel compensation matrix to obtain a target channel compensation matrix; and adopting the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal. According to the technical scheme of the embodiment of the invention, the deployment cost of a physical layer can be reduced, the expandability of signal estimation is improved, the calculation overhead is reduced, the signal compensation precision is improved, the bit error rate of a receiving end is remarkably reduced, the communication rate is improved, and the stability requirements of a current communication system on high rate, low time delay and high reliability can be met.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a channel compensation method, device, electronic equipment and storage medium. Background Art

[0002] The existing channel estimation algorithms are mainly divided into reference signal-based channel estimation, blind estimation and semi-blind estimation. Among them, the reference signal-based channel estimation algorithm is the most widely used. The reference signal-based channel estimation algorithm calculates and processes the receiving end signal and the transmitting end signal to obtain the channel compensation matrix, thereby restoring the original signal.

[0003] However, the current wireless channel environment is becoming increasingly complex, and traditional channel estimation algorithms have insufficient scalability and high computational overhead, and can no longer meet the current communication system's requirements for high speed, low latency, and high reliability. Summary of the invention

[0004] The present invention provides a channel compensation method, device, electronic device and storage medium, which can reduce the physical layer deployment cost, improve the scalability of signal estimation, reduce computing overhead, improve signal compensation accuracy, significantly reduce the bit error rate of the receiving end, and increase the communication rate, which can meet the current communication system's requirements for high rate, low latency and high reliability.

[0005] According to one aspect of the present invention, a channel compensation method is provided, the method comprising:

[0006] Obtaining a receiving end pilot signal and a transmitting end pilot signal;

[0007] Using an initial channel estimation algorithm, performing signal estimation on the receiving end pilot signal and the transmitting end pilot signal to obtain an initial channel compensation matrix;

[0008] Based on a pre-trained channel compensation matrix update model, the initial channel compensation matrix is ​​modified to obtain a target channel compensation matrix;

[0009] The target signal compensation matrix is ​​used to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

[0010] According to another aspect of the present invention, there is provided a channel compensation device, the device comprising:

[0011] A pilot signal acquisition module, used to acquire a receiving end pilot signal and a transmitting end pilot signal;

[0012] An initial channel estimation module, used to use an initial channel estimation algorithm to perform signal estimation on the receiving end pilot signal and the transmitting end pilot signal to obtain an initial channel compensation matrix;

[0013] A compensation matrix updating module, used to modify the initial channel compensation matrix based on a pre-trained channel compensation matrix updating model to obtain a target channel compensation matrix;

[0014] The channel compensation module is used to use the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the channel compensation method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the channel compensation method described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the channel compensation method according to any embodiment of the present invention is implemented.

[0021] The technical solution of the embodiment of the present invention obtains a receiving end pilot signal and a transmitting end pilot signal, adopts an initial channel estimation algorithm, performs signal estimation on the receiving end pilot signal and the transmitting end pilot signal, obtains an initial channel compensation matrix, corrects the initial channel compensation matrix based on a pre-trained channel compensation matrix update model, obtains a target channel compensation matrix, uses the target signal compensation matrix to perform channel compensation on the receiving end pilot signal, and obtains an original pilot signal. The powerful nonlinear characterization capability of the pre-trained channel compensation matrix update model is utilized, and the calculation speed is fast, so that the shortcomings of the initial channel compensation matrix can be improved. It can reduce the physical layer deployment cost, improve the scalability of signal estimation, reduce computing overhead, improve signal compensation accuracy, significantly reduce the bit error rate of the receiving end, and improve the communication rate. In addition, compared with directly performing model-based channel estimation on the transmitter signal and the receiver signal, the technical solution of the embodiment of the present invention adopts the initial channel estimation based on the initial channel estimation algorithm and the secondary channel estimation based on the channel compensation matrix update model. It can also take into account the advantages of the initial channel estimation algorithm in ensuring the robustness and anti-interference capability of the physical layer communication link, and can meet the current communication system's stability requirements for high rate, low latency and high reliability.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 is a flow chart of a channel compensation method provided according to Embodiment 1 of the present invention;

[0025] Figure 2 is a flow chart of a channel compensation method provided according to Embodiment 2 of the present invention;

[0026] Figure 3 is a flow chart of a channel compensation method provided according to Embodiment 2 of the present invention;

[0027] Figure 4A is a schematic diagram of a pilot signal at a transmitting end provided according to Embodiment 2 of the present invention;

[0028] Figure 4Bis a schematic diagram of a pilot signal at a receiving end provided according to Embodiment 2 of the present invention;

[0029] Figure 5 is a structural schematic diagram of a channel compensation device provided according to Embodiment 3 of the present invention;

[0030] Figure 6 It is a structural schematic diagram of an electronic device for implementing the channel compensation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 A flow chart of a channel compensation method provided in Embodiment 1 of the present invention. The embodiment of the present invention is applicable to the case where channel compensation is performed on a pilot signal at a receiving end. The method can be performed by a channel compensation device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device that carries a channel estimation function.

[0035] See also Figure 1 The channel compensation method shown comprises:

[0036] S110: Acquire a receiving end pilot signal and a transmitting end pilot signal.

[0037] In the process of signal transmission, there is a transmitting device and a receiving device. The pilot signal of the transmitting end can be a pilot signal sent by the transmitting device. The pilot signal of the receiving end can be a pilot signal received by the receiving device. In the process of signal transmission, it is inevitable to be interfered by the signal, which causes the pilot signal of the receiving end to be distorted, making it difficult to ensure the accuracy of signal transmission.

[0038] Specifically, a signal acquisition device may be used to acquire signals from a transmitting-end device and a receiving-end device to obtain a receiving-end pilot signal and a transmitting-end pilot signal.

[0039] In an optional embodiment of the present invention, obtaining a receiving end pilot signal and a transmitting end pilot signal includes: obtaining a receiving end signal and a transmitting end signal; performing inverse Fourier transform on the receiving end signal and the transmitting end signal respectively to obtain a first receiving end signal and a first transmitting end signal; performing Heisenberg transform on the first receiving end signal and the first transmitting end signal respectively to obtain a second receiving end signal and a second transmitting end signal; performing Wigner transform on the second receiving end signal and the second transmitting end signal respectively to obtain a third receiving end signal and a third transmitting end signal; performing sparse fast Fourier transform on the third receiving end signal and the third transmitting end signal respectively to obtain a receiving end signal. The invention relates to a pilot signal and a transmitting end pilot signal; wherein the transmitting end pilot signal comprises a transmitting end reference pilot signal, a transmitting end first pilot signal and a transmitting end second pilot signal; the transmitting end reference pilot signal is used for channel compensation evaluation; after adopting a target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal, the invention also comprises: comparing an original reference pilot signal contained in the original pilot signal with the transmitting end reference pilot signal; and processing an original first pilot signal and an original second pilot signal contained in the original pilot signal according to the pilot signal comparison result to obtain a data processing result of the original pilot signal.

[0040] The receiving end signal may be a signal actually received by the receiving end device. The transmitting end signal may be a signal actually sent by the transmitting end device. The receiving end signal and the transmitting end signal may be time Doppler domain signals. The first receiving end signal may be an inverse Fourier transform result of the receiving end signal. The first transmitting end signal may be an inverse Fourier transform result of the transmitting end signal. The first receiving end signal and the first transmitting end signal may be time-frequency domain signals. The second receiving end signal may be a Heisenberg transform result of the first receiving end signal. The second transmitting end signal may be a Heisenberg transform result of the second transmitting end signal. The second receiving end signal and the second transmitting end signal may be time domain signals. The third receiving end signal may be a Wigner transform result of the second receiving end signal. The third transmitting end signal may be a Wigner transform result of the second transmitting end signal. The third receiving end signal and the third transmitting end signal may be time-frequency domain signals. The receiving end pilot signal may be a sparse fast Fourier transform result of the third receiving end signal. The transmitting end pilot signal may be a sparse fast Fourier transform result of the third transmitting end signal. The receiving end pilot signal and the transmitting end pilot signal may be time Doppler domain signals. The reference pilot signal of the transmitting end can be used to evaluate the signal compensation result of the pilot signal of the receiving end. Exemplarily, the reference pilot signal of the transmitting end can be the peak signal of the central range of the pilot signal of the transmitting end. The first pilot signal of the transmitting end and the second pilot signal of the transmitting end can be used to characterize the information required to be transmitted by the transmitting end signal. Among them, the first pilot signal of the transmitting end can be the peak signal of other ranges in the pilot signal of the transmitting end except the central range. The second pilot signal of the transmitting end can be the valley signal of other ranges in the pilot signal of the transmitting end except the central range. The reference pilot signal of the receiving end can be used to characterize the signal interference degree of the pilot signal of the receiving end. The first pilot signal of the receiving end and the second pilot signal of the receiving end can be used to characterize the information received by the receiving end signal. Among them, the first pilot signal of the receiving end can be the peak signal of other ranges in the pilot signal of the receiving end except the central range. The second pilot signal of the receiving end can be the valley signal of other ranges in the pilot signal of the receiving end except the central range. The original reference pilot signal can be used to evaluate the signal compensation result of the pilot signal of the receiving end. The original first pilot signal and the original second pilot signal can be used to characterize the information transmitted after signal compensation. Among them, the original first pilot signal can be a peak signal in other ranges except the center range in the original pilot signal. The original second pilot signal can be a valley signal in other ranges except the center range in the original pilot signal. The pilot signal comparison result is the comparison result between the reference pilot signal of the transmitting end and the original reference pilot signal. Exemplarily, the pilot signal comparison result may include the quantity comparison result and amplitude comparison result between the reference pilot signal of the transmitting end and the original reference pilot signal. The pilot signal comparison result can be used to characterize the signal compensation effect of the pilot signal of the receiving end.It can be understood that the closer the pilot signal comparison results are, the better the signal compensation effect of the pilot signal at the receiving end is; the more different the pilot signal comparison results are, the worse the signal compensation effect of the pilot signal at the receiving end is. The data processing result can be the signal processing result of the original first pilot signal and the original second pilot signal contained in the original pilot signal.

[0041] Specifically, a transmitting end signal sent by a transmitting end device and a receiving end signal received by a receiving end device can be obtained. The receiving end signal and the transmitting end signal can be inversely Fourier transformed to obtain a first receiving end signal and a first transmitting end signal. The first receiving end signal and the first transmitting end signal can be Heisenberg transformed to obtain a second receiving end signal and a second transmitting end signal. The second receiving end signal and the second transmitting end signal can be Wigner transformed to obtain a third receiving end signal and a third transmitting end signal. The third receiving end signal and the third transmitting end signal can be sparsely fast Fourier transformed to obtain a receiving end pilot signal and a transmitting end pilot signal. After the target signal compensation matrix is ​​used to perform channel compensation on the pilot signal at the receiving end to obtain the original pilot signal, the original reference pilot signal contained in the original pilot signal can be compared with the reference pilot signal at the transmitting end. When the pilot signal comparison result shows that the reference pilot signal at the transmitting end and the original reference pilot signal are close, the original first pilot signal and the original second pilot signal contained in the original pilot signal can be processed to obtain the data processing result of the original pilot signal; when the pilot signal comparison result shows that the reference pilot signal at the transmitting end and the original reference pilot signal are not close, the original first pilot signal and the original second pilot signal contained in the original pilot signal can be not processed.

[0042] This scheme pre-generates a transmitting-end pilot signal and a receiving-end pilot signal by performing inverse Fourier transform, Heisenberg transform, Wigner transform and sparse fast Fourier transform on the receiving-end signal and the transmitting-end signal respectively, realizes the pre-division of reference information and transmission information in the transmitting-end signal and the receiving-end signal, realizes the evaluation of the signal compensation effect of the receiving-end pilot signal by comparing the original reference pilot signal contained in the original pilot signal with the transmitting-end reference pilot signal, and obtains the data processing result of the original pilot signal by processing the original first pilot signal and the original second pilot signal contained in the original pilot signal according to the pilot signal comparison result, thereby further improving the data processing accuracy of the original pilot signal.

[0043] S120 , using an initial channel estimation algorithm to perform signal estimation on a pilot signal at a receiving end and a pilot signal at a transmitting end, and obtaining an initial channel compensation matrix.

[0044] The initial channel estimation algorithm can be used to perform preliminary channel estimation on the receiving end pilot signal and the transmitting end pilot signal respectively. Exemplarily, the initial channel estimation algorithm can be a reference signal-based channel estimation algorithm, such as LS (Least Squares) or MMSE (Minimum Mean Square Error). The initial channel compensation matrix can be a channel compensation matrix generated by the initial channel estimation algorithm.

[0045] Due to the limitations of the initial channel estimation algorithm, the initial channel compensation matrix has the problems of insufficient scalability and high computational overhead, and can no longer meet the current communication system's stability requirements for high speed, low latency and high reliability. However, the initial channel estimation algorithm has the advantages of ensuring the robustness and anti-interference capability of the physical layer communication link. This scheme does not directly perform model-based channel estimation on the transmitter signal and the receiver signal, but instead performs a primary channel estimation based on the initial channel estimation algorithm and a secondary channel estimation based on the channel compensation matrix update model. This can take into account the advantages of the initial channel estimation algorithm while further improving the shortcomings of the initial channel compensation matrix.

[0046] Specifically, an initial channel estimation algorithm may be used to calculate a pilot signal at the receiving end and a pilot signal at the transmitting end to obtain an initial channel compensation matrix.

[0047] S130 , based on a pre-trained channel compensation matrix update model, correct the initial channel compensation matrix to obtain a target channel compensation matrix.

[0048] The channel compensation matrix update model can be used to correct the initial channel compensation matrix. The input data of the channel compensation matrix update model is the initial channel compensation matrix, and the output result can be the target channel compensation matrix. Exemplarily, the channel compensation matrix update model may include a convolutional neural network model, a random forest, a reinforcement learning model, or a width learning model. The target channel compensation matrix may be a correction result of the initial channel compensation matrix. Compared with the initial channel compensation matrix, the compensation effect of the target channel compensation matrix is ​​better. The target channel compensation matrix can be used to perform signal compensation on the receiving end pilot signal.

[0049] Specifically, the initial channel compensation matrix may be input into a pre-trained channel compensation matrix, and the initial channel compensation matrix may be corrected to obtain a target channel compensation matrix.

[0050] In an optional embodiment of the present invention, based on a pre-trained channel compensation matrix update model, the initial channel compensation matrix is ​​corrected to obtain a target channel compensation matrix, including: performing data cleaning and data normalization on the initial channel compensation matrix, and updating the initial channel compensation matrix; based on the pre-trained channel compensation matrix update model, the updated initial channel compensation matrix is ​​corrected to obtain the target channel compensation matrix.

[0051] Specifically, the initial channel compensation matrix may be cleaned, for example, by filtering out noise, filling missing values, and / or detecting based on a Z-score algorithm, and the initial channel compensation matrix may be updated. The updated initial channel compensation matrix may be normalized, for example, by dividing each element in the initial channel compensation matrix by the maximum value of the element in the initial channel compensation matrix, so that the data is normalized, the data range is controlled within 0-1, and the initial channel compensation matrix is ​​updated again. The updated initial channel compensation matrix may be input into a channel compensation matrix update model, and a target channel compensation matrix may be output.

[0052] This scheme introduces data cleaning of the initial channel compensation matrix, realizes outlier processing of the initial channel compensation matrix, introduces data normalization of the initial channel compensation matrix, realizes data alignment of each element in the initial channel compensation matrix, and further improves the accuracy of the target channel compensation matrix obtained based on the secondary correction of the initial channel compensation matrix, thereby improving the accuracy of channel compensation, and further improving the accuracy of data transmission between the transmitting device and the receiving device.

[0053] S140: Use the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

[0054] The original pilot signal may be a channel compensation result of the receiving end pilot signal. It can be understood that in the process of signal transmission, signal interference is inevitable, and the obtained receiving end pilot signal has signal distortion. The original pilot signal may be a pilot signal obtained by restoring the distorted receiving end pilot signal, thereby realizing the recovery of the receiving end pilot signal.

[0055] Specifically, the target signal pilot matrix and the receiving end pilot signal may be calculated to obtain the original pilot signal.

[0056] In an optional embodiment of the present invention, a target signal compensation matrix is ​​used to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal, including: multiplying the target channel compensation matrix by the receiving end pilot signal to obtain the original pilot signal to achieve recovery of the receiving end pilot signal.

[0057] Specifically, the target channel compensation matrix and the pilot signal at the receiving end may be directly multiplied to obtain the original pilot signal, thereby realizing the recovery of the pilot signal at the receiving end.

[0058] This solution obtains the original pilot signal by multiplying the target channel compensation matrix with the pilot signal at the receiving end, thereby simplifying the calculation process of the original pilot signal and improving the recovery efficiency of the original pilot signal.

[0059] The technical solution of the embodiment of the present invention obtains a receiving end pilot signal and a transmitting end pilot signal, adopts an initial channel estimation algorithm, performs signal estimation on the receiving end pilot signal and the transmitting end pilot signal, obtains an initial channel compensation matrix, corrects the initial channel compensation matrix based on a pre-trained channel compensation matrix update model, obtains a target channel compensation matrix, uses the target signal compensation matrix to perform channel compensation on the receiving end pilot signal, and obtains an original pilot signal. The powerful nonlinear characterization capability of the pre-trained channel compensation matrix update model is utilized, and the calculation speed is fast, so that the shortcomings of the initial channel compensation matrix can be improved. It can reduce the physical layer deployment cost, improve the scalability of signal estimation, reduce computing overhead, improve signal compensation accuracy, significantly reduce the bit error rate of the receiving end, and improve the communication rate. In addition, compared with directly performing model-based channel estimation on the transmitter signal and the receiver signal, the technical solution of the embodiment of the present invention adopts the initial channel estimation based on the initial channel estimation algorithm and the secondary channel estimation based on the channel compensation matrix update model. It can also take into account the advantages of the initial channel estimation algorithm in ensuring the robustness and anti-interference capability of the physical layer communication link, and can meet the current communication system's stability requirements for high rate, low latency and high reliability.

[0060] Embodiment 2

[0061] Figure 2 A flow chart of a channel compensation method provided for the second embodiment of the present invention. Based on the above embodiments, the embodiment of the present invention further adds "in an offline state, obtaining a training sample set; wherein the training sample set includes an actual channel compensation matrix and an initial channel compensation matrix sample; inputting the initial channel compensation matrix sample in the training sample set into the channel compensation matrix update model to obtain an estimated channel compensation matrix; based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, adjusting the parameters of the channel compensation matrix update model", by pre-training the channel compensation matrix update model offline and using the channel compensation matrix update model to calculate the target channel compensation matrix online, the calculation efficiency of the target channel compensation matrix is ​​further improved, and the data communication efficiency between the transmitting device and the receiving device can be further improved. It should be noted that for the parts not described in detail in the embodiments of the present invention, please refer to the description of other embodiments.

[0062] See also Figure 2 The channel compensation method shown comprises:

[0063] S210: Obtain a training sample set in an offline state.

[0064] The offline state is used to characterize that the device is in an offline state. It can be understood that the offline state can be used to characterize that the device offline trains the channel compensation matrix update model, online calculates the target channel compensation matrix, and performs channel compensation on the receiving end pilot signal. The training sample set can be used to train the channel compensation matrix update model. The training sample set may include an actual channel compensation matrix and an initial channel compensation matrix sample. Among them, the actual channel compensation matrix can be used as a sample label for the channel compensation matrix update model. The initial channel compensation matrix sample can be an initial channel estimation result of the transmitting end pilot signal sample and the receiving end pilot signal sample using an initial channel estimation algorithm. The transmitting end pilot signal sample can be a pilot signal of the transmitting end device used as a training sample. The receiving end pilot signal sample can be a pilot signal of the receiving end device used as a training sample.

[0065] Specifically, the pilot signal samples of the transmitting end and the pilot signal samples of the receiving end can be obtained in an offline state. The initial channel estimation algorithm can be used to perform signal estimation on the pilot signal samples of the transmitting end and the pilot signal samples of the receiving end to obtain the initial channel compensation matrix samples. The actual channel compensation matrix calibrated in advance can be obtained. The actual channel compensation matrix and the initial channel compensation matrix samples can be used as training sample sets.

[0066] S220 , inputting the initial channel compensation matrix samples in the training sample set into the channel compensation matrix update model to obtain an estimated channel compensation matrix.

[0067] The estimated channel compensation matrix may be an initial channel compensation matrix predicted by a channel compensation matrix update model based on samples of the initial channel compensation matrix.

[0068] Specifically, the initial channel compensation matrix samples in the training sample set may be input into the channel compensation matrix update model, and channel estimation is performed on the initial channel compensation matrix samples to obtain an estimated channel compensation matrix.

[0069] S230: Adjust parameters of a channel compensation matrix update model based on a difference between an actual channel compensation matrix and an estimated channel compensation matrix.

[0070] Specifically, the loss value can be calculated based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, such as mean square error (MSE) or cross entropy loss. The parameters of the channel compensation matrix update model can be adjusted with the goal of minimizing or converging the loss value. After the channel compensation matrix update model is trained, it can be used to correct the initial channel compensation matrix.

[0071] S240: Acquire a receiving end pilot signal and a transmitting end pilot signal.

[0072] S250: using an initial channel estimation algorithm to perform signal estimation on a pilot signal at the receiving end and a pilot signal at the transmitting end to obtain an initial channel compensation matrix.

[0073] S260: Based on the pre-trained channel compensation matrix update model, the initial channel compensation matrix is ​​corrected to obtain a target channel compensation matrix.

[0074] S270: Use the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

[0075] The technical solution of the embodiment of the present invention obtains a training sample set in an offline state, inputs the initial channel compensation matrix samples in the training sample set into a channel compensation matrix update model to obtain an estimated channel compensation matrix, adjusts the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, and further improves the calculation efficiency of the target channel compensation matrix by pre-training the channel compensation matrix update model offline and using the channel compensation matrix update model to calculate the target channel compensation matrix online, which can further improve the data communication efficiency between the transmitting device and the receiving device.

[0076] In an optional embodiment of the present invention, the channel compensation matrix update model is a deep neural network model; after adjusting the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, it also includes: obtaining a verification sample set; wherein the data type of the verification sample set is the same as that of the training sample set; using a K-fold cross-validation algorithm to verify the adjusted channel compensation matrix based on the verification sample set to obtain at least one cross-validation parameter; comparing the cross-validation parameters, and determining the parameters of the channel compensation matrix update model corresponding to the minimum value of the cross-validation parameter as the optimal parameters of the channel compensation matrix update model.

[0077] The deep neural network model has powerful nonlinear computing capabilities. The validation sample set can be used to update the parameters of the channel compensation matrix model. The validation sample set has the same data type as the training sample set. It can be understood that the validation sample set may also include actual channel compensation matrices and initial channel compensation matrix samples. There may be a preset sample ratio between the training sample set and the validation sample set. Exemplarily, the preset sample ratio may be 4:1. This allows the training and validation of the channel compensation matrix update model. The cross-validation parameter may be a single validation parameter of the parameters of the channel compensation matrix update model by a K-fold cross-validation algorithm. The cross-validation parameter may be used to characterize the pros and cons of the channel compensation matrix update model corresponding to a single training. The minimum cross-validation parameter may be used to characterize the optimal parameters of the channel compensation matrix update model.

[0078] Specifically, after adjusting the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, the verification sample set can be obtained in the same manner as the training sample set. A K-fold cross-validation algorithm can be used to verify the parameters of the channel compensation matrix update model after each single parameter adjustment based on the verification sample set to obtain the cross-validation parameters corresponding to each single validation. The cross-validation parameters are compared, and the parameters of the channel compensation matrix update model corresponding to the minimum cross-validation parameter are determined as the optimal parameters of the channel compensation matrix update model.

[0079] This scheme further improves the correction efficiency of the channel compensation matrix update model by verifying the parameters of the channel compensation matrix update model during the training process of the channel compensation matrix update model, thereby improving the channel compensation accuracy and data transmission accuracy based on the channel compensation matrix update model.

[0080] Figure 3 FIG. 1 is a flow chart of a channel compensation method. Based on the above embodiment, Figure 3 This is a preferred embodiment of the present invention. This solution is mainly divided into an offline training module and an online calculation module. For the offline training module, by using traditional channel estimation algorithms such as LS or MMSE, the initial channel compensation matrix can be obtained by calculating the pilot signal of the transmitting end and the pilot signal of the receiving end. Among them, the pilot structure selects a block structure. Figure 4A As shown, the transmitting end pilot signal may include a transmitting end reference pilot signal, a transmitting end first pilot signal and a transmitting end second pilot signal. The transmitting end reference pilot signal may be located in the center of the data, and a guard interval is set to prevent signal interference between the transmitting end reference pilot signal, the transmitting end first pilot signal and the transmitting end second pilot signal. Figure 4BAs shown, the receiving end pilot signal may include a receiving end reference pilot signal, a receiving end first pilot signal and a receiving end second pilot signal. The receiving end reference pilot signal may be located in the center of the data, and a protection interval is set to prevent signal interference between the receiving end reference pilot signal, the receiving end first pilot signal and the receiving end second pilot signal. Figure 4A and Figure 4B As shown, the pilot signal at the transmitting end has experienced signal interference such as Doppler frequency shift, and the pilot signal at the receiving end has different degrees of distortion, especially the reference pilot signal at the receiving end. The actual channel compensation matrix and the estimated channel compensation matrix obtained by the initial channel estimation algorithm can be used together to form the original data sample for deep neural network training. Among them, the sample label is the actual channel compensation matrix. Optionally, the original data set can be simulated and generated according to the original data sample. The original data set can be divided into a training sample set and a verification sample set according to 4:1. The training sample set can be preprocessed. For example, data cleaning and data normalization are performed. Among them, data cleaning can filter out noise in the training sample set, fill in missing values, and detect and process outliers based on the Z-score algorithm. Data normalization can limit the data size to within the range of 0-1, and divide each training sample by the maximum value of the element in the sample to normalize the data. The training sample set can be used to train the deep neural network model, and the parameters of the deep neural network model can be verified by the K-fold cross-validation algorithm. When the minimum RMSE (i.e., the minimum value of the cross-validation parameter) is obtained, the optimal solution for the parameters of the deep neural network model is obtained. Among them, the deep neural network model can be selected from the third-party library Keras (Advanced Neural Network API), the number of neural network layers of the deep neural network model can be set to three layers, the number of neurons are 128, 64 and 16 respectively, the learning rate is set to 0.001, and the parameter optimization algorithm is RULA (Rapid Upper Limb Assessment). The deep neural network model updates the parameters through continuous iteration optimization. After the deep neural network model training is completed, the model structure and weight parameters are saved as HDF5 files. For the online calculation module, the initial channel compensation matrix obtained by the initial channel estimation algorithm in the actual scene can be used as input to the deep neural network model calculation to obtain the target channel compensation matrix. This target channel compensation matrix is ​​multiplied with the receiving end pilot signal in sequence to restore the original pilot signal, thereby achieving the effect of reducing the bit error rate.

[0081] The present invention mainly solves the problems of poor scalability, high computational overhead and inability to meet the needs of current wireless communication systems in traditional channel estimation algorithms. It utilizes the advantages of deep neural network models, such as powerful nonlinear characterization capabilities and fast computational speed, and applies them to channel estimation and equalization technology. It can reduce the physical layer deployment cost, improve signal compensation accuracy, significantly reduce the bit error rate at the receiving end, and increase the communication rate.

[0082] Embodiment 3

[0083] Figure 5 A schematic diagram of the structure of a channel compensation device provided in Embodiment 3 of the present invention. The embodiment of the present invention can be applied to the case where channel compensation is performed on a pilot signal at a receiving end, the device can execute a channel compensation method, the device can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device carrying a channel estimation function.

[0084] See also Figure 5 The channel compensation device shown includes: a pilot signal acquisition module 510, an initial channel estimation module 520, a compensation matrix update module 530 and a channel compensation module 540. The pilot signal acquisition module 510 is used to acquire a receiving end pilot signal and a transmitting end pilot signal; the initial channel estimation module 520 is used to use an initial channel estimation algorithm to perform signal estimation on the receiving end pilot signal and the transmitting end pilot signal to obtain an initial channel compensation matrix; the compensation matrix update module 530 is used to update a pre-trained channel compensation matrix model to correct the initial channel compensation matrix to obtain a target channel compensation matrix; the channel compensation module 540 is used to use the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

[0085] The technical solution of the embodiment of the present invention obtains the pilot signal of the receiving end and the pilot signal of the transmitting end, adopts the initial channel estimation algorithm, performs signal estimation on the pilot signal of the receiving end and the pilot signal of the transmitting end, obtains the initial channel compensation matrix, and based on the pre-trained channel compensation matrix update model, corrects the initial channel compensation matrix to obtain the target channel compensation matrix, and uses the target signal compensation matrix to perform channel compensation on the pilot signal of the receiving end to obtain the original pilot signal. The powerful nonlinear characterization ability of the pre-trained channel compensation matrix update model is utilized, and the calculation speed is fast. The shortcomings of the initial channel compensation matrix can be improved, the physical layer deployment cost can be reduced, the scalability of signal estimation can be improved, the calculation overhead can be reduced, the signal compensation accuracy can be improved, the bit error rate of the receiving end can be significantly reduced, and the communication rate can be improved. In addition, compared with directly performing model-based channel estimation on the transmitting end signal and the receiving end signal, the technical solution of the embodiment of the present invention adopts the initial channel estimation based on the initial channel estimation algorithm and the secondary channel estimation based on the channel compensation matrix update model, and can also take into account the advantages of the initial channel estimation algorithm that can ensure the robustness and anti-interference ability of the physical layer communication link, and can meet the stability requirements of the current communication system for high rate, low delay and high reliability.

[0086] In an optional embodiment of the present invention, the device also includes: a training sample set acquisition module, used to acquire a training sample set in an offline state; wherein the training sample set includes an actual channel compensation matrix and an initial channel compensation matrix sample; an estimated compensation matrix generation module, used to input the initial channel compensation matrix sample in the training sample set into a channel compensation matrix update model to obtain an estimated channel compensation matrix; a model parameter adjustment module, used to adjust the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix.

[0087] In an optional embodiment of the present invention, the channel compensation matrix update model is a deep neural network model; the device also includes: a verification sample set acquisition module, which is used to obtain a verification sample set after adjusting the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix; wherein the verification sample set has the same data type as that in the training sample set; a model parameter verification module, which is used to use a K-fold cross-validation algorithm to verify the adjusted channel compensation matrix based on the verification sample set to obtain at least one cross-validation parameter; a model training completion module, which is used to compare each of the cross-validation parameters and determine the parameters of the channel compensation matrix update model corresponding to the minimum value of the cross-validation parameter as the optimal parameters of the channel compensation matrix update model.

[0088] In an optional embodiment of the present invention, the pilot signal acquisition module 510 includes: acquiring a receiving end signal and a transmitting end signal; a first signal conversion unit, used to perform inverse Fourier transform on the receiving end signal and the transmitting end signal, respectively, to obtain a first receiving end signal and a first transmitting end signal; a second signal conversion unit, used to perform Heisenberg transform on the first receiving end signal and the first transmitting end signal, respectively, to obtain a second receiving end signal and a second transmitting end signal; a third signal conversion unit, used to perform Wigner transform on the second receiving end signal and the second transmitting end signal, respectively, to obtain a third receiving end signal and a third transmitting end signal; a fourth signal conversion unit, used to perform sparse fast transform on the third receiving end signal and the third transmitting end signal, respectively. Fourier transform is performed to obtain a receiving end pilot signal and a transmitting end pilot signal; wherein the transmitting end pilot signal includes a transmitting end reference pilot signal, a transmitting end first pilot signal and a transmitting end second pilot signal; the transmitting end reference pilot signal is used to perform channel compensation evaluation; the device also includes: a pilot signal comparison module, which is used to compare the original reference pilot signal contained in the original pilot signal with the transmitting end reference pilot signal after performing channel compensation on the receiving end pilot signal using the target signal compensation matrix to obtain the original pilot signal; and a data processing module, which is used to process the original first pilot signal and the original second pilot signal contained in the original pilot signal according to the pilot signal comparison result to obtain the data processing result of the original pilot signal.

[0089] In an optional embodiment of the present invention, the channel compensation module 540 includes: a channel compensation unit, which is used to multiply the target channel compensation matrix with the receiving end pilot signal to obtain the original pilot signal to achieve recovery of the receiving end pilot signal.

[0090] In an optional embodiment of the present invention, the compensation matrix update module 530 includes: a data preprocessing unit, used to perform data cleaning and data normalization on the initial channel compensation matrix, and update the initial channel compensation matrix; a compensation matrix updating unit, used to correct the updated initial channel compensation matrix based on a pre-trained channel compensation matrix update model to obtain a target channel compensation matrix.

[0091] The channel compensation device provided in the embodiment of the present invention can execute the channel compensation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0092] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of the receiving end pilot signal, transmitting end pilot signal, training sample set, actual channel compensation matrix, initial channel compensation matrix sample, receiving end signal and transmitting end signal, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0093] Embodiment 4

[0094] Figure 6 A schematic diagram of the structure of an electronic device 600 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0095] like Figure 6 As shown, the electronic device 600 includes at least one processor 601, and a memory connected to the at least one processor 601, such as a read-only memory (ROM) 602, a random access memory (RAM) 603, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 601 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 602 or the computer program loaded from the storage unit 608 to the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0096] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0097] The processor 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 601 performs the various methods and processes described above, such as a channel compensation method.

[0098] In some embodiments, the channel compensation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the processor 601, one or more steps of the channel compensation method described above may be performed. Alternatively, in other embodiments, the processor 601 may be configured to perform the channel compensation method in any other appropriate manner (e.g., by means of firmware).

[0099] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0101] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0104] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A channel compensation method, characterized in that: The method comprises: Obtaining a receiving end pilot signal and a transmitting end pilot signal; Using an initial channel estimation algorithm, performing signal estimation on the receiving end pilot signal and the transmitting end pilot signal to obtain an initial channel compensation matrix; Based on a pre-trained channel compensation matrix update model, the initial channel compensation matrix is ​​modified to obtain a target channel compensation matrix; The target signal compensation matrix is ​​used to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

2. The method according to claim 1, characterized in that Also includes: In an offline state, a training sample set is obtained; wherein the training sample set includes an actual channel compensation matrix and an initial channel compensation matrix sample; Inputting the initial channel compensation matrix samples in the training sample set into a channel compensation matrix update model to obtain an estimated channel compensation matrix; Based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, parameters of the channel compensation matrix update model are adjusted.

3. The method according to claim 2, characterized in that The channel compensation matrix update model is a deep neural network model; After adjusting the parameters of the channel compensation matrix update model based on the difference between the actual channel compensation matrix and the estimated channel compensation matrix, the method further includes: Acquire a verification sample set; wherein the verification sample set has the same data type as that in the training sample set; Using a K-fold cross validation algorithm, the channel compensation matrix after parameter adjustment is validated based on the validation sample set to obtain at least one cross validation parameter; The cross-validation parameters are compared, and the parameters of the channel compensation matrix update model corresponding to the minimum value of the cross-validation parameters are determined as the optimal parameters of the channel compensation matrix update model.

4. The method according to claim 1, characterized in that: The obtaining of the receiving end pilot signal and the transmitting end pilot signal comprises: Obtain receiving end signal and transmitting end signal; Performing inverse Fourier transform on the receiving end signal and the transmitting end signal respectively to obtain a first receiving end signal and a first transmitting end signal; Performing Heisenberg transform on the first receiving end signal and the first transmitting end signal respectively to obtain a second receiving end signal and a second transmitting end signal; Performing Wigner transform on the second receiving end signal and the second transmitting end signal respectively to obtain a third receiving end signal and a third transmitting end signal; Performing sparse fast Fourier transform on the third receiving end signal and the third transmitting end signal respectively to obtain a receiving end pilot signal and a transmitting end pilot signal; wherein the transmitting end pilot signal includes a transmitting end reference pilot signal, a transmitting end first pilot signal and a transmitting end second pilot signal; the transmitting end reference pilot signal is used for channel compensation evaluation; After the target signal compensation matrix is ​​used to perform channel compensation on the receiving end pilot signal to obtain the original pilot signal, the method further includes: Comparing an original reference pilot signal contained in the original pilot signal with a transmitting end reference pilot signal; According to the pilot signal comparison result, the original first pilot signal and the original second pilot signal contained in the original pilot signal are processed to obtain the data processing result of the original pilot signal.

5. The method according to claim 1, characterized in that The using the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal includes: The target channel compensation matrix is ​​multiplied by the receiving end pilot signal to obtain an original pilot signal, so as to realize recovery of the receiving end pilot signal.

6. The method according to claim 1, characterized in that The updating model based on the pre-trained channel compensation matrix is ​​used to modify the initial channel compensation matrix to obtain the target channel compensation matrix, including: Performing data cleaning and data normalization on the initial channel compensation matrix, and updating the initial channel compensation matrix; Based on a pre-trained channel compensation matrix update model, the updated initial channel compensation matrix is ​​corrected to obtain a target channel compensation matrix.

7. A channel compensation device, characterized in that: The device comprises: A pilot signal acquisition module, used to acquire a receiving end pilot signal and a transmitting end pilot signal; An initial channel estimation module, used to use an initial channel estimation algorithm to perform signal estimation on the receiving end pilot signal and the transmitting end pilot signal to obtain an initial channel compensation matrix; A compensation matrix updating module, used to modify the initial channel compensation matrix based on a pre-trained channel compensation matrix updating model to obtain a target channel compensation matrix; The channel compensation module is used to use the target signal compensation matrix to perform channel compensation on the receiving end pilot signal to obtain an original pilot signal.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the channel compensation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the channel compensation method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the channel compensation method according to any one of claims 1 to 6.

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