Channel state information feedback compression method based on massive MIMO system

Abstract

The present invention discloses a channel state information feedback compression method based on a large-scale MIMO system, comprising the following steps: obtaining a historical CSI matrix, constructing a time and space dual-domain channel change rate quantification index, and using a time convolutional network to predict the future change rate; establishing a block granularity mapping function based on the prediction result, dynamically adjusting the block granularity of the CSI matrix, and generating a submatrix; adopting an adaptive dual-mode compression strategy based on the dynamic characteristics of the channel, using an improved Kronecker sparse representation in dynamic scenarios and an attention-enhanced autoencoder in static scenarios; optimizing the compressed submatrix feedback process through a joint codebook and a closed-loop feedback mechanism; and reducing the CSI feedback overhead through a block strategy and a compression mode selection mechanism, while ensuring high-quality reconstruction and transmission of the CSI, and being suitable for complex and changeable channel environments.

Description

Technical Field

[0001] The present invention relates to the field of channel state information processing technology, and in particular to a channel state information feedback compression method based on a large-scale MIMO system. Background Art

[0002] With the rapid development of fifth-generation mobile communication technology (5G), Massive Multiple Input Multiple Output (Massive MIMO) technology has become the core supporting technology for the new generation of wireless communication systems due to its breakthrough advantages in spectrum efficiency, energy efficiency and system capacity. This technology configures a large-scale antenna array on the base station side and uses spatial resources to achieve precise beamforming and efficient spatial multiplexing, thereby significantly improving system throughput and user service quality. However, the system performance is highly dependent on the accuracy of Channel State Information (CSI), which faces severe challenges in actual deployment. Specifically, the user equipment (UE) needs to feed back the high-dimensional CSI matrix to the base station via the uplink. The exponential growth of antenna scale and the number of subcarriers has led to an order of magnitude increase in the dimension of the CSI matrix, which has generated huge feedback overhead pressure and seriously restricted the effective utilization of system bandwidth resources.

[0003] To address the critical issue of CSI feedback compression, the academic community has proposed a variety of solutions, including sparse reconstruction methods based on compressed sensing (CS), intelligent coding schemes powered by deep neural networks (DNNs), and dimensionality reduction strategies based on matrix decomposition. However, existing methods still have significant limitations in engineering practice: First, traditional CS methods are based on a theoretical framework that assumes channel sparsity. However, in actual communication environments, factors such as multipath effects, time-varying fading, and non-ideal scattering cause the sparse characteristics of the channel to exhibit dynamic uncertainty, resulting in significant degradation in reconstruction performance. Second, existing deep learning methods are mostly static models that do not fully consider the dynamic characteristics of the channel in time and space, making it difficult to achieve adaptive compression in dynamic scenarios. Third, traditional matrix decomposition methods typically use a fixed block strategy and cannot dynamically adjust the block granularity based on channel variations, making it difficult to balance compression efficiency and reconstruction quality.

[0004] Therefore, it is urgent to propose a CSI feedback compression method that can fully consider the spatiotemporal dynamic characteristics of the channel and implement adaptive blocking and compression strategies, so as to effectively reduce the feedback overhead and improve the channel reconstruction accuracy. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a channel state information feedback compression method based on a large-scale MIMO system to solve the problems raised in the background technology.

[0007] To solve the above technical problems, the present invention provides the following technical solution: a channel state information feedback compression method based on a massive MIMO system, comprising:

[0008] Obtaining a historical channel state information matrix, constructing a dual-domain channel change rate quantification index, and designing a temporal convolutional network prediction module based on the dual-domain channel change rate quantification index to predict future dual-domain channel change rate quantification indicators;

[0009] Establishing a block granularity mapping function based on the predicted future dual-domain channel change rate quantitative index, dynamically adjusting the block granularity of the channel state information matrix according to the block granularity mapping function, and obtaining a sub-matrix of the channel state information after blocking;

[0010] The sub-matrix is ​​compressed, and a feedback process of the compressed sub-matrix is ​​optimized to ensure efficient transmission and high-quality reconstruction of channel state information.

[0011] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system according to the present invention, the process of obtaining a historical channel state information matrix and constructing a dual-domain channel change rate quantization indicator includes:

[0012] Considering the rate of change of the historical channel state information in spatial and temporal dimensions;

[0013] The time dimension change rate is obtained by comparing the differences between adjacent time slots, and the spatial dimension change rate is obtained by calculating the channel response power variance of each antenna pair on all subcarriers.

[0014] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system described in the present invention, wherein: using the dual-domain channel change rate quantization index, a temporal convolutional network prediction module is designed to predict the future dual-domain channel change rate quantization index, including:

[0015] Extract features of the time dimension change rate and the space dimension change rate based on the causal dilation convolution in the time convolutional network, and predict the future time dimension change rate and the space dimension change rate;

[0016] The predicted future time dimension change rate and future space dimension change rate are input into the offline reinforcement learning model to obtain the future time dimension change rate threshold, future space dimension change rate threshold and hyperparameters.

[0017] The causal expansion convolution is a multi-layer convolution structure, each layer of the convolution structure contains a different expansion factor, and the expansion factor of each layer is 2 l Incrementally, k is the number of layers.

[0018] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system described in the present invention, a block granularity mapping function is established based on the predicted future dual-domain channel change rate quantization index, including:

[0019] According to the hyperparameters, the predicted future time dimension change rate and future space dimension change rate are mapped to the block size.

[0020] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system described in the present invention, wherein: according to the block granularity mapping function, the block granularity of the channel state information matrix is ​​dynamically adjusted to obtain a sub-matrix of the channel state information after blocking, including:

[0021] Obtaining a current channel state information matrix and creating a block strategy, wherein the block strategy is divided into a spatial domain block strategy and a frequency domain block strategy;

[0022] In the spatial domain blocking strategy, the current channel state information matrix is ​​divided by the block size, and an overlapping blocking method is adopted according to the remaining size after the division;

[0023] In the frequency domain blocking strategy, the future spatial dimension change rate is judged by means of a preset threshold. If the future spatial dimension change rate is greater than the preset threshold, non-uniform blocking is adopted; otherwise, uniform blocking is adopted.

[0024] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system according to the present invention, compressing the submatrix includes:

[0025] The compression method is divided into two modes: dynamic scene and static scene;

[0026] For the dynamic scene, an improved Kronecker sparse representation is used, and for the static scene, an attention-enhanced autoencoder is used.

[0027] As a preferred solution of the channel state information feedback compression method based on a large-scale MIMO system according to the present invention, it further includes:

[0028] Define dynamic scene and static scene judgment functions. When the future time dimension change rate is greater than the future time dimension change rate threshold and the future space dimension change rate is greater than the future space dimension change rate threshold, the compression method adopts the dynamic scene, otherwise the static scene is adopted.

[0029] As a preferred solution of the channel state information feedback compression method based on a large-scale MIMO system described in the present invention, the improved Kronecker sparse representation includes:

[0030] A joint dictionary is constructed, where the joint dictionary consists of a time dictionary and a space dictionary, and the time dictionary is generated by Hankel matrix decomposition.

[0031] As a preferred solution of the channel state information feedback compression method based on the massive MIMO system described in the present invention, the attention-enhanced autoencoder includes:

[0032] A spatial-frequency attention gate is introduced into the autoencoder.

[0033] As a preferred solution of the channel state information feedback compression method based on a massive MIMO system according to the present invention, the feedback process of the sub-matrix after optimization and compression includes:

[0034] A triplet-structured joint codebook is obtained according to the block size, compression method, and quantization step size, and the compressed submatrix is ​​mapped to the joint codebook as an index in the codebook;

[0035] The compressed sub-matrix is ​​reconstructed using the joint codebook and index, and a reconstructed error value is calculated. If the error value is greater than 0, feedback is triggered, and the reconstruction information is fed back to the temporal convolutional network prediction module to continuously optimize and predict the future dual-domain channel change rate quantization index and the compression process of the sub-matrix until the error value is 0.

[0036] Compared with the prior art, the invention has the following beneficial effects:

[0037] 1. This invention constructs a dual-domain channel change rate quantification indicator and uses a temporal convolutional network (TCN) to predict future change trends, dynamically sensing channel changes. This method breaks away from the traditional method's reliance on sparsity assumptions, can adapt to complex and changing environments, and ensures the stability of compression performance.

[0038] 2. Since traditional matrix decomposition methods use a fixed block strategy and cannot be flexibly adjusted according to channel changes, it is difficult to strike a balance between compression efficiency and reconstruction quality. The present invention uses a dynamic block mechanism to optimize the block granularity according to channel characteristics, refining the blocks in areas with drastic changes and coarsening the blocks in stable areas, thus ensuring compression efficiency while ensuring reconstruction accuracy.

[0039] 3. In addition, by establishing a scenario decision function to dynamically select the compression mode, we ensure that the optimal strategy can be adopted under different channel conditions. By creating a joint codebook, we maintain the accuracy of CSI reconstruction while reducing the compression feedback overhead, ensuring the stability and reliability of the system in long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0041] Figure 1 This is an overall flow chart of a channel state information feedback compression method based on a massive MIMO system according to one embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0045] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0046] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0047] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0048] Example 1

[0049] Reference Figure 1 , which is the first embodiment of the present invention, provides a channel state information feedback compression method based on a massive MIMO system, including:

[0050] S1. Obtain a historical channel state information matrix, construct a dual-domain channel change rate quantification index, and design a temporal convolutional network prediction module based on the dual-domain channel change rate quantification index to predict the future dual-domain channel change rate quantification index;

[0051] Specifically, in a massive MIMO system, a pilot signal is sent by a transmitter, and a receiver (such as a user equipment UE) receives the pilot signal and calculates a channel estimate. The calculated channel estimate result is stored as a channel state information matrix H k , and record the channel state information matrix in order of time slots;

[0052] It should be noted that the pilot signal is a known sequence periodically sent by the transmitting end, and the receiving end calculates the channel estimate by using these signals. The channel estimate can adopt the minimum mean square error MMSE or zero-forcing ZF method;

[0053] Specifically, H kIt is represented as the historical channel state information matrix at the kth time slot, and its dimension is expressed as:

[0054]

[0055] Where C is the complex field, which means that each element in the complex field contains a real part and an imaginary part; N tx Expressed as the number of transmitting antennas; N rx Expressed as the number of receiving antennas; N sc Expressed as the number of subcarriers;

[0056] Specifically, the historical channel state information matrix H k Normalizing the amplitude, we get:

[0057]

[0058] in,‖·‖ F Expressed as the Frobenius norm to ensure the stability of the channel state information matrix during normalization;

[0059] Furthermore, the change rate of the historical channel state information in the spatial and temporal dimensions is considered;

[0060] It should be noted that since the channel environment fluctuates due to changes in time and space, such as user movement and the distribution of antenna arrays, these changes need to be quantified. Specifically, rate of change indicators are defined in the time and space dimensions, namely the time dimension rate of change and the space dimension rate of change. The time dimension rate of change is used to measure the speed of channel change over a continuous time period, and the space dimension rate of change is used to measure the spatial variability of the channel. The channel response between different antennas may vary significantly.

[0061] Furthermore, by comparing the differences between adjacent time slots, the time dimension change rate is obtained, and by calculating the variance of the channel response power of each antenna pair on all subcarriers, the spatial dimension change rate is obtained;

[0062] Specifically, the time dimension change rate R t Expressed as:

[0063]

[0064] Among them, H k (i) is represented as the historical channel state information matrix of the i-th subcarrier at the k-th time slot, where K is the time window length, indicating the time range of observation;

[0065] It should be noted that if R t If R is large, it means that the channel changes quickly, for example, when the user moves at high speed; tIf it is smaller, it means the channel is relatively stable;

[0066] Specifically, the spatial dimension change rate R s Expressed as:

[0067]

[0068] Where Var(·) represents the variance, which calculates the power variation of each antenna pair on all subcarriers; h m,n (f) represents the channel response of the mth transmitting antenna and the nth receiving antenna on the i-th subcarrier;

[0069] It should be noted that if R s If R s If it is smaller, it means that the spatial distribution is more uniform;

[0070] It should be noted that it is not enough to just know the rate of change of the time dimension and the spatial dimension. It is also necessary to predict the rate of change of the time dimension and the spatial dimension in the future in order to prepare for dynamic block segmentation and compression method selection. To this end, a prediction module based on the temporal convolutional network (TCN) is designed to predict the rate of change of the time dimension and the spatial dimension.

[0071] It should be explained that TCN is a neural network that specializes in processing time series. Through causal dilated convolution, it can capture long-term dependencies in historical data while avoiding the gradient vanishing problem that may be encountered in traditional recurrent neural networks (such as LSTM).

[0072] Furthermore, based on the causal dilation convolution in the temporal convolutional network, feature extraction is performed on the temporal dimension change rate and the spatial dimension change rate, and the future temporal dimension change rate and the spatial dimension change rate are predicted;

[0073] Specifically, the causal dilation convolution is a multi-layer convolution structure, each layer of the convolution structure contains a different dilation factor, and the dilation factor of each layer is 2 l Increment, l is the number of layers;

[0074] It should be noted that by expanding the dilation factor, the receptive field of the temporal convolutional network can be gradually expanded, allowing it to see further into the past.

[0075] Specifically, the mathematical formula for predicting the future rate of change of the time dimension and the space dimension can be expressed as:

[0076]

[0077] Among them, Rt-kΔt Expressed as the time rate of change of the kth time slot, R s-kΔt Expressed as the spatial variation rate of the kth time slot; It is expressed as a predicted value, including the predicted results of the change rate of future time dimension and space dimension; L is expressed as a historical time slot;

[0078] Furthermore, the predicted future time dimension change rate and future space dimension change rate are input into the offline reinforcement learning model to obtain the future time dimension change rate threshold, future space dimension change rate threshold and hyperparameters;

[0079] It should be explained that reinforcement learning (RL) is a machine learning method for optimizing decision-making, while offline reinforcement learning refers to using historical data or simulation environments to generate strategies during the training phase and directly applying them in actual deployment without the need for real-time online training.

[0080] Specifically, the reinforcement learning framework includes: state: the characteristics of the current channel environment, action: the specific threshold of the change rate of the future time dimension and the future spatial dimension, and reward: designed according to the performance indicators of the system;

[0081] Specifically, during the training phase, a large number of "state-action-reward" samples are generated by using the historical channel state information matrix or a simulation environment. The reinforcement learning model is then trained to learn to select the optimal thresholds for the rate of change of the future time dimension and the future spatial dimension under different states. In practical applications, because this training is done offline, the computational overhead during deployment is low.

[0082] S2. Establishing a block granularity mapping function based on the predicted future dual-domain channel change rate quantitative index, and dynamically adjusting the block granularity of the channel state information matrix according to the block granularity mapping function to obtain a sub-matrix of the channel state information after block division;

[0083] Furthermore, the predicted future time dimension change rate and future space dimension change rate are mapped to the block size using the hyperparameters, and a block granularity mapping function is established based on the mapping process to the block size.

[0084] Specifically, the block granularity mapping function is expressed as:

[0085]

[0086] Among them, B represents the block size, α, β, and γ are hyperparameters used to balance the impact of temporal and spatial change rates and adapt to different scenarios; B max The maximum allowed chunk size, which limits the upper limit of chunks; represents the floor function; exp(·) represents the natural exponential function, which is used to ensure that the channel variation is smooth and nonlinear;

[0087] It should be noted that when the channel changes rapidly ( and When the channel size is large, the block size B will be reduced accordingly to capture more details; similarly, when the channel changes slowly, the block size B will be increased to improve compression efficiency;

[0088] Further, obtaining the current channel state information matrix;

[0089] Specifically, the current channel state information matrix is ​​obtained, whose dimension is the same as the historical channel state information matrix H k Remain consistent, but with time slot k = K + 1;

[0090] It should be explained that since the size of the current channel state information matrix obtained may not be an integer multiple of the block size B, it is necessary to consider how to process the remaining part of the block;

[0091] Furthermore, a block strategy is created, wherein the block strategy is divided into a spatial domain block strategy and a frequency domain block strategy;

[0092] Furthermore, in the spatial domain (antenna dimension) blocking strategy, the current channel state information matrix is ​​divided by the blocking size, and an overlapping blocking method is adopted according to the remaining size after the division;

[0093] Specifically, the current channel state information matrix is ​​divided into B×B blocks. For example, if B=4, the matrix is ​​divided into multiple 4×4 sub-blocks. If there are remaining blocks after the division or the size is incorrect during the division process, an overlapping block method is used;

[0094] Specifically, the overlapping block allows for partial overlap between adjacent sub-blocks. For example, assuming the number of transmit antennas is 64, the receive antenna dimension is 4, and the block size is 4×4, if the receive antenna dimension 4 is less than 16, for example, 4×3, a complete sub-block cannot be formed. In this case, the entire receive antenna dimension is treated as a sub-block for superposition.

[0095] Furthermore, in the frequency domain (subcarrier dimension) blocking strategy, the future spatial dimension change rate is judged by a preset threshold. If the future spatial dimension change rate is greater than the preset threshold, non-uniform blocking is adopted, otherwise uniform blocking is adopted;

[0096] It should be noted that if the future spatial dimension change rate is greater than the preset threshold, it indicates that the spatial change is large and there is obvious frequency selective fading in the frequency domain. On the contrary, if the future spatial dimension change rate is greater than the preset threshold, it indicates that the spatial change is relatively gentle and uniform blocking is suitable.

[0097] Specifically, the preset threshold value can be obtained by statistical analysis, simulation experiments, machine learning or expert experience;

[0098] Specifically, uniform blocking refers to dividing the channel state information matrix into sub-blocks of equal size in a certain dimension; non-uniform blocking refers to dividing the data into sub-blocks of different sizes based on the high-frequency and low-frequency changes of the channel state information matrix in the frequency domain, for example, using larger sub-blocks in the low-frequency area and smaller sub-blocks in the high-frequency area;

[0099] It should be noted that the sub-matrix refers to the channel state information matrix after block division, which is composed of multiple sub-blocks;

[0100] S3. compressing the submatrix and optimizing the feedback process of the compressed submatrix to ensure efficient transmission and high-quality reconstruction of channel state information;

[0101] Furthermore, the compression method of the submatrix is ​​divided into two modes: dynamic scene and static scene;

[0102] It should be noted that, since different channel dynamics require different compression strategies, two compression methods are designed in the solution of the present invention and adaptively switched according to the scenario decision function;

[0103] Furthermore, the dynamic scene adopts the improved Kronecker sparse representation, and the static scene adopts the attention-enhanced autoencoder;

[0104] Specifically, for the improved Kronecker sparse representation, a joint dictionary is constructed;

[0105] Specifically, the joint dictionary D is expressed as:

[0106]

[0107] Among them, D s Represented as a spatial dictionary, it is used to capture the sparse features of the CSI matrix in the spatial dimension; Expressed as Kronecker product; D t Represented as a time dictionary, it is used to capture the sparse features of the CSI matrix in the time dimension and is generated by Hankel matrix decomposition:

[0108]

[0109] in, Expressed as the reconstruction error term, λ∥X∥ 1,2 is represented as a regularization term; argmin is represented as the minimization objective; H(H t) represents the conversion of the channel state information matrix Ht in the time dimension into a Hankel matrix; X is the sparse coefficient matrix; λ is the penalty coefficient, which controls the trade-off between reconstruction error and sparsity. The larger λ is, the stronger the sparsity is, and the smaller λ is, the smaller the reconstruction error is.

[0110] Specifically, for the attention-enhanced autoencoder, a space-frequency attention gate is introduced into the autoencoder;

[0111] Specifically, the space-frequency attention gate A is expressed as follows:

[0112] A=σ(W a [H s ;H f ]+b a )

[0113] Among them, σ(·) represents the Sigmoid activation function, which is used to map the output of the attention weight matrix to the (0,1) interval, [H s ;H f ] represents the spatial feature H s With frequency characteristics H f Splicing; W a Represented as attention weight matrix; b a Expressed as a bias term;

[0114] It should be noted that b a By linear transformation W a [H s ;H f ]Apply an offset to ensure that the attention mechanism can more accurately capture the importance of spatial and frequency features;

[0115] Specifically, by defining dynamic scene and static scene decision functions, when the future time dimension change rate is greater than the future time dimension change rate threshold and the future space dimension change rate is greater than the future space dimension change rate threshold, the compression method adopts the dynamic scene, otherwise the static scene is adopted;

[0116] Furthermore, a triplet-structured joint codebook is obtained according to the block size, compression method, and quantization step size, and the compressed submatrix is ​​mapped to the joint codebook as an index in the codebook;

[0117] It should be explained that in communication systems, a codebook is a predefined set of symbols used to map data into transmittable signals. In CSI feedback, the codebook represents the compressed CSI as a series of indices, which significantly reduces the amount of data required for transmission.

[0118] Specifically, the joint codebook F of the triplet structure is expressed as:

[0119] F={(B,mode,Q)}

[0120] Among them, mode represents the compression method, static scene or dynamic scene; Q represents the quantization step size, which is expressed as:

[0121]

[0122] Among them, Q min is the minimum value of the quantization step, Q max is the maximum value of the quantization step, R t,min is the minimum value of the rate of change in the time dimension, R t,max is the maximum value of the rate of change in the time dimension;

[0123] It should be noted that the quantization step size can be based on The size of is adaptively adjusted to maintain a balance between transmission overhead and reconstruction accuracy;

[0124] It should be noted that in order to ensure the quality of the CSI reconstructed at the receiving end, the reconstructed CSI needs to be evaluated;

[0125] Furthermore, the compressed sub-matrix is ​​reconstructed using the joint codebook and index, and a reconstructed error value is calculated. If the error value is greater than 0, feedback is triggered, and the reconstruction information is fed back to the temporal convolutional network prediction module to continuously optimize and predict the future dual-domain channel change rate quantization index and the compression process of the sub-matrix until the error value is 0.

[0126] Specifically, the evaluation formula of the reconstructed error value ∈ is as follows:

[0127]

[0128] Among them, H(i) is the original CSI, For the reconstructed CSI;

[0129] Specifically, when the feedback is triggered, the receiving end notifies the transmitting end, and the transmitting end fine-tunes the parameters in the temporal convolutional network (TCN) model according to the error value reconstructed in the trigger feedback, and repeats the above process, re-predicting and compressing until the error value is 0.

[0130] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0135] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A channel state information feedback compression method based on a massive MIMO system, characterized in that: include: Obtaining a historical channel state information matrix, constructing a dual-domain channel change rate quantification index, and designing a temporal convolutional network prediction module based on the dual-domain channel change rate quantification index to predict future dual-domain channel change rate quantification indicators; Establishing a block granularity mapping function based on the predicted future dual-domain channel change rate quantitative index, dynamically adjusting the block granularity of the channel state information matrix according to the block granularity mapping function, and obtaining a sub-matrix of the channel state information after blocking; The sub-matrix is ​​compressed, and a feedback process of the compressed sub-matrix is ​​optimized to ensure efficient transmission and high-quality reconstruction of channel state information.

2. The channel state information feedback compression method based on a massive MIMO system according to claim 1, wherein: The step of obtaining a historical channel state information matrix and constructing a dual-domain channel change rate quantitative indicator includes: Considering the rate of change of the historical channel state information in spatial and temporal dimensions; The time dimension change rate is obtained by comparing the differences between adjacent time slots, and the spatial dimension change rate is obtained by calculating the channel response power variance of each antenna pair on all subcarriers.

3. The channel state information feedback compression method based on a massive MIMO system according to claim 2, wherein: Based on the dual-domain channel change rate quantification index, a temporal convolutional network prediction module is designed to predict the future dual-domain channel change rate quantification index, including: Extract features of the time dimension change rate and the space dimension change rate based on the causal dilation convolution in the time convolutional network, and predict the future time dimension change rate and the space dimension change rate; The predicted future time dimension change rate and future space dimension change rate are input into the offline reinforcement learning model to obtain the future time dimension change rate threshold, future space dimension change rate threshold and hyperparameters; The causal expansion convolution is a multi-layer convolution structure, each layer of the convolution structure contains a different expansion factor, and the expansion factor of each layer is 2 l Incrementally, l is the number of layers.

4. The channel state information feedback compression method based on a massive MIMO system according to claim 3, wherein: Based on the predicted quantitative indicators of the future dual-domain channel change rate, a block granularity mapping function is established, including: According to the hyperparameters, the predicted future time dimension change rate and future space dimension change rate are mapped to the block size.

5. The channel state information feedback compression method based on a massive MIMO system according to claim 4, wherein: According to the block granularity mapping function, dynamically adjusting the block granularity of the channel state information matrix to obtain a sub-matrix of the channel state information after blocking, including: Obtaining a current channel state information matrix and creating a block strategy, wherein the block strategy is divided into a spatial domain block strategy and a frequency domain block strategy; In the spatial domain blocking strategy, the current channel state information matrix is ​​divided by the block size, and an overlapping blocking method is adopted according to the remaining size after the division; In the frequency domain blocking strategy, the future spatial dimension change rate is judged by means of a preset threshold. If the future spatial dimension change rate is greater than the preset threshold, non-uniform blocking is adopted; otherwise, uniform blocking is adopted.

6. The channel state information feedback compression method based on a massive MIMO system according to claim 5, wherein: Compressing the submatrix includes: The compression method is divided into two modes: dynamic scene and static scene; For the dynamic scene, an improved Kronecker sparse representation is used, and for the static scene, an attention-enhanced autoencoder is used.

7. The channel state information feedback compression method based on a massive MIMO system according to claim 3 or 6, characterized in that: Also includes: Define dynamic scene and static scene judgment functions. When the future time dimension change rate is greater than the future time dimension change rate threshold and the future space dimension change rate is greater than the future space dimension change rate threshold, the compression method adopts the dynamic scene, otherwise the static scene is adopted.

8. The channel state information feedback compression method based on a massive MIMO system according to claim 6, wherein: The improved Kronecker sparse representation includes: A joint dictionary is constructed, where the joint dictionary consists of a time dictionary and a space dictionary, and the time dictionary is generated by Hankel matrix decomposition.

9. The channel state information feedback compression method based on a massive MIMO system according to claim 6, wherein: The attention-enhanced autoencoder comprises: A spatial-frequency attention gate is introduced into the autoencoder.

10. The channel state information feedback compression method based on a massive MIMO system according to claim 4 or 6, characterized in that: The feedback process of the sub-matrix after optimization compression includes: A triplet-structured joint codebook is obtained according to the block size, compression method, and quantization step size, and the compressed submatrix is ​​mapped to the joint codebook as an index in the codebook; The compressed sub-matrix is ​​reconstructed using the joint codebook and index, and a reconstructed error value is calculated. If the error value is greater than 0, feedback is triggered, and the reconstruction information is fed back to the temporal convolutional network prediction module to continuously optimize and predict the future dual-domain channel change rate quantization index and the compression process of the sub-matrix until the error value is 0.

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