Spatial Non-Stationary Massive MIMO Channel Estimation Method Based on Prescreening and Multilevel Dynamic Threshold Strategy

Abstract

The present invention discloses a spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategies, belonging to the field of wireless communication technologies, and solving the technical problems of insufficient channel estimation accuracy and high computational complexity in existing spatial non-stationary large-scale MIMO systems. This channel estimation method is divided into two stages: pre-screening strategy stage estimation, which preliminarily screens all possible candidate atoms to reduce the computational amount; by calculating the correlation between the candidate atoms and the residual, the candidate atoms most likely to be non-zero are selected to form a primary candidate set; multi-level dynamic threshold adjustment stage estimation, which further screens the candidate atom set selected by the pre-selection strategy and improves the accuracy of non-zero element estimation through a dynamic threshold mechanism; the dynamic threshold is adjusted according to the current signal-to-noise ratio (SNR) to adaptively handle the estimation accuracy under different channel conditions and ensure the robustness of channel estimation under different SNR conditions.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to a spatial non-stationary massive MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategies. Background Art

[0002] With the development of 5G and future 6G communication technologies, the application of massive multiple-input multiple-output (Massive MIMO) technology in wireless communication systems has become increasingly widespread. The Massive MIMO system realizes spatial multiplexing through a large-scale antenna array, thereby significantly improving the spectral efficiency, communication capacity, and transmission reliability of the system. Due to these advantages, massive MIMO is considered one of the key technologies for future communication systems.

[0003] However, with the expansion of the scale of the base station antenna array, the channel model and channel estimation method face new challenges, especially in a spatial non-stationary environment. The emergence of a spatial non-stationary channel is due to the large physical size of the antenna array, and the channel environments experienced by different parts of the array are different, which makes the signals received by different antennas have different statistical characteristics. Specifically, different parts of the antenna array may be exposed to different scattering clusters, which results in the channel characteristics between antennas showing spatial non-stationarity. Therefore, traditional channel estimation methods, such as the least squares method (LS) and the minimum mean square error method (MMSE), are unable to accurately estimate the channel state when facing such a complex non-stationary channel, resulting in performance degradation.

[0004] Most existing channel estimation methods assume that the channel has a uniform sparse structure, but in a spatial non-stationary channel, the sparsity of the channel usually shows a non-uniform distribution, resulting in poor performance of traditional estimation algorithms in this scenario. Commonly used compressive sensing methods, such as orthogonal matching pursuit (OMP) and sparse adaptive matching pursuit (SAMP), perform poorly when dealing with non-uniform sparse channels and are difficult to obtain high-precision channel estimates. In addition, although some methods such as sparse Bayesian learning (SBL) improve the estimation accuracy, their high computational complexity limits their application in actual large-scale systems.

[0005] Facing the problem of spatial non-stationary channels in massive MIMO systems, there is an urgent need for an efficient and robust channel estimation method that can accurately estimate the non-uniform sparse structure in spatial non-stationary channels while maintaining a relatively low computational complexity. Summary of the Invention

[0006] In view of the drawbacks and deficiencies in the prior art, the present invention provides a hierarchical channel estimation method based on pre-screening and multi-level dynamic threshold strategies. By dividing the channel estimation process into two levels and combining the pre-screening strategy and dynamic threshold adjustment, it can adaptively estimate the channel sparse structure in a spatially non-stationary environment, achieving adaptive processing of channel sparsity and non-uniformity, thereby significantly improving the accuracy and efficiency of channel estimation. A spatially non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategies.

[0007] This method processes the channel estimation process through hierarchical processing.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: The spatially non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategies provided by the present invention includes the following steps:

[0009] S1 Establishment of the system model;

[0010] S2 Hierarchical channel estimation method, dividing the channel estimation process into two stages: pre-screening strategy stage estimation and multi-level dynamic threshold adjustment stage estimation;

[0011] S2.1 Pre-screening strategy stage estimation, first initially screening all possible candidate atoms to reduce the computational amount; the main purpose of this estimation stage is to select the candidate atoms most likely to be non-zero by calculating the correlation between the candidate atoms and the residual, and form a primary candidate set;

[0012] S2.2 Multi-level dynamic threshold adjustment stage estimation, further screening the candidate atom set selected by the pre-screening strategy, and improving the accuracy of non-zero atom estimation through a dynamic threshold mechanism; the dynamic threshold is adjusted according to the current signal-to-noise ratio (SNR) to adaptively process the estimation accuracy under different channel conditions and ensure the robustness of channel estimation under different SNR conditions;

[0013] S3 Through the channel estimation process of the pre-screening strategy stage estimation and multi-level dynamic threshold adjustment stage estimation in step S2, obtain the final output of the channel estimation result.

[0014] Preferably, the establishment of the system model in step S1 specifically includes the following:

[0015] This method is applicable to a large-scale MIMO system. It is assumed that the system uses orthogonal frequency division multiplexing (OFDM) technology for signal transmission; the base station has a large-scale antenna array and receives signals from multiple users. The channel sparsity is reflected in that some antennas receive effective signals;

[0016] The received signal is expressed as:

[0017] (1)

[0018] Among them, is the received signal matrix, is the sensing matrix, is the channel matrix, is the noise matrix.

[0019] Preferably, in the hierarchical channel estimation method in step 2, for the first-stage estimation based on the pre-screening strategy, 2.1 pre-screening strategy stage estimation, the goal of this process is to reduce the computational complexity in channel estimation and achieve more accurate and efficient channel estimation by screening the potential non-zero atom candidate set; the specific steps are as follows:

[0020] Initialization: Initialize the channel estimation matrix as a zero matrix, and the residual matrix as the received signal matrix , and the initial support set ;

[0021] (1) Calculate the correlation: Calculate the correlation between each candidate atom and the current residual. The residual matrix is expressed as:

[0022] (2)

[0023] Among them, is the channel estimation value at the th iteration;

[0024] (2) Select the primary candidate set: According to the correlation ranking, perform pre-screening to select the part of atoms with the highest correlation to form the primary candidate set; the correlation is calculated by the following formula:

[0025] (3)

[0026] Among them, is the th column of the sensing matrix, is the current residual matrix;

[0027] (3) Update the candidate support set: The selected primary candidate set is used to update the current support set. The updated support set is:

[0028] (4)

[0029] Among them, is the support set in the previous iteration, is the candidate atom set obtained through pre-screening.

[0030] Preferably, it further includes:

[0031] (4)Channel estimation update: Using the updated support set, re-estimate the channel matrix , and update the residual:

[0032] (5)

[0033] where is the pseudo-inverse of the sensing matrix corresponding to the support set, is the received signal matrix;

[0034] (5)Stopping condition: If the preset number of iterations is reached or the residual is less than the set threshold, the iteration ends and the estimated channel matrix is output.

[0035] Preferably, the pre-screening strategy in step (2) is: According to the atomic correlation ranking, the top atoms are selected to enter the primary candidate set; the pre-screening strategy determines the number of selected atoms through a preset ratio ratio, which is set to 10% of the total number of candidate atoms, that is:

[0036] (6)

[0037] where is the total number of atoms.

[0038] Preferably, in the hierarchical channel estimation method in step 2, the second-stage estimation based on multi-level dynamic threshold modulation, 2.2 multi-dynamic threshold adjustment stage estimation, this method accurately estimates non-zero atoms by dynamically adjusting the threshold to ensure the robustness of channel estimation under different signal-to-noise ratio conditions. The specific steps are as follows:

[0039] Initialization: Using the channel matrix output by the first-stage estimation as the input, initialize the multi-level dynamic threshold parameter ;

[0040] (1)Calculate the signal-to-noise ratio: First, calculate the current signal-to-noise ratio based on the current received signal and the noise matrix :

[0041] (7)

[0042] (2)Dynamic threshold adjustment: Calculate the dynamic threshold adjustment factor according to the current signal-to-noise ratio and the target signal-to-noise ratio. The adjustment factor is calculated by the following formula:

[0043] (8)

[0044] The subscript "current" in the formula represents the current signal-to-noise ratio, and "target" represents the given target signal-to-noise ratio. This factor is used to adjust the dynamic threshold under different SNR conditions to ensure better noise suppression effect under low SNR conditions and improve the channel estimation accuracy under high SNR conditions;

[0045] And dynamically modulate the threshold To adapt to the current channel conditions:

[0046] (9)

[0047] Where Is the number of non-zero atoms, Is the support set, Is the sensing matrix corresponding to the support set; Is the variance of the channel noise; The superscript Represents the conjugate transpose.

[0048] Preferably, it further includes:

[0049] (3) Accurately screen non-zero atoms: Use the adjusted threshold , further screen the non-zero atoms estimated in the first stage, remove the pseudo non-zero atoms introduced by noise interference, and update the support set and the estimated value;

[0050] (4) Result output: After completing all iterations, output the final channel estimation matrix .

[0051] Preferably, it further includes that in step S3, the specific steps are as follows:

[0052] Through a two-stage channel estimation process, the final channel estimation result is obtained, expressed as:

[0053] (10)

[0054] Where, Is the estimation parameter adjusted according to the dynamic threshold, Is the received signal matrix, Is the sensing matrix.

[0055] Preferably, it further includes the simulation verification in step S4. The performance of this estimation method is verified through simulation analysis. The specific performance indicators include the number of atom selections, the normalized mean square error (NMSE), and the reconstruction success probability.

[0056] The present invention provides a spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategies. It has the following beneficial effects:

[0057] (1) The spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy of the present invention can adaptively estimate the channel sparse structure in a spatial non-stationary environment by hierarchically processing the channel estimation process, combining the pre-screening strategy and dynamic threshold adjustment, significantly improving the efficiency and accuracy of channel estimation, achieving efficient and accurate estimation of the channel, effectively solving the problem of spatial non-stationary channels in large-scale MIMO systems, reducing the performance degradation problem encountered by traditional compressive sensing algorithms when dealing with non-uniform sparse structures, and being applicable to complex wireless communication scenarios.

[0058] (2) Compared with traditional channel estimation algorithms, the present invention has significant advantages in the following aspects: Reduced computational complexity: Through the pre-screening strategy, the number of atoms to be processed is reduced in the preliminary screening of candidate atoms, thus reducing the computational complexity.

[0059] Enhanced adaptability: The dynamic threshold adjustment mechanism adaptively adjusts the estimation parameters according to the current signal-to-noise ratio, enabling the method to maintain high robustness under different channel conditions.

[0060] Improved estimation accuracy: The method can more accurately capture the non-uniform sparse structure in the spatial non-stationary channel, significantly improving the accuracy of channel estimation, especially having better noise resistance in low signal-to-noise ratio scenarios. Description of the Drawings

[0061] Figure 1 It is a comparison diagram of the advantages of the method of the present invention in terms of the number of atom selections.

[0062] Figure 2 It is a comparison diagram of the mean square error and signal-to-noise ratio performance between the method of the present invention and other methods.

[0063] Figure 3 It is a comparison diagram of the reconstruction success probability between the method of the present invention and other methods. Specific Embodiments

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Embodiment 1

[0065] The spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy of the present invention includes the following steps:

[0066] Establishment of the S1 system model; this method is applicable to large-scale MIMO systems. It is assumed that the system uses orthogonal frequency division multiplexing (OFDM) technology for signal transmission; the base station has a large-scale antenna array and receives signals from multiple users. The channel sparsity is reflected in that some antennas receive valid signals.

[0067] The received signal is expressed as:

[0068] (1)

[0069] Where is the received signal matrix, is the sensing matrix, is the channel matrix, is the noise matrix.

[0070] S2 Hierarchical channel estimation method, which divides the channel estimation process into two stages: pre-screening strategy stage estimation and multi-level dynamic threshold adjustment stage estimation; 2.1 Pre-screening strategy stage estimation. In this estimation stage, first, all possible candidate atoms are preliminarily screened to reduce the computational amount; the main purpose of this estimation stage is to select the candidate atoms most likely to be non-zero by calculating the correlation between the candidate atoms and the residual, and form a primary candidate set; 2.2 Multi-level dynamic threshold adjustment stage estimation. Further screening is performed on the candidate atom set selected by the pre-screening strategy, and the accuracy of non-zero atom estimation is improved through a dynamic threshold mechanism; the dynamic threshold is adjusted according to the current signal-to-noise ratio (SNR) to adaptively handle the estimation accuracy under different channel conditions and ensure the robustness of channel estimation under different SNR conditions.

[0071] S3 Through the channel estimation process of the pre-screening strategy stage estimation and the multi-level dynamic threshold adjustment stage estimation in step S2, the final channel estimation result is obtained. Embodiment 2

[0072] First-stage estimation based on the pre-screening strategy

[0073] 2.1 Pre-screening strategy stage estimation. The goal of this process is to reduce the computational complexity in channel estimation and achieve more accurate and efficient channel estimation by screening the potential non-zero atom candidate set; the specific steps are as follows:

[0074] (1) Initialization: Initialize the channel estimation matrix as a zero matrix, the residual matrix as the received signal matrix , and the initial support set ;

[0075] (2) Calculate the correlation between the atom and the residual: Calculate the correlation between each candidate atom and the current residual, and the residual matrix Expressed as:

[0076] (2)

[0077] Wherein, is the channel estimation value at the th iteration;

[0078] (3) Select the primary candidate set: According to the correlation ranking, perform pre-screening to select the most relevant part of the atoms to form the primary candidate set; the correlation is calculated by the following formula:

[0079] (3)

[0080] Wherein, is the th column of the sensing matrix, is the current residual matrix;

[0081] In each iteration, calculate the inner product of each column in the sensing matrix and the residual matrix to obtain the atomic correlation of the current iteration:

[0082] . (4)

[0083] The pre-screening strategy is: According to the atomic correlation ranking, screen out the top atoms to enter the primary candidate set; the pre-screening strategy determines the number of selected atoms through a preset ratio ratio, which is set to 10% of the total number of candidate atoms, that is:

[0084] (5)

[0085] Wherein is the total number of atoms.

[0086] (4) Update the candidate support set: The selected primary candidate set is used to update the current support set, and the updated support set is:

[0087] (6)

[0088] Wherein, is the support set in the previous iteration, is the candidate atom set obtained through pre-screening.

[0089] (5) Channel estimation update: Use the updated support set to re-estimate the channel matrix and update the residual:

[0090] (7)

[0091] where is the pseudo-inverse of the sensing matrix corresponding to the support set, is the received signal matrix;

[0092] Stopping condition: If the preset number of iterations is reached or the residual is less than the set threshold, the iteration ends and the estimated channel matrix is output .

[0093] Through the pre-screening strategy, the candidate atom set selected reduces the unnecessary number of atoms, thereby reducing the computational complexity. Especially in the case of low signal-to-noise ratio, this method exhibits higher accuracy and convergence speed.

[0094] Pre-screening strategy stage: In the initial stage of channel estimation, the pre-screening strategy is adopted to screen the candidate atoms that may contain non-zero atoms. By calculating the correlation between the candidate atoms and the current residual, the atom set most likely to be non-zero atoms is preferentially selected, thereby reducing the computational amount and accelerating the estimation process. Embodiment 3

[0095] Second-stage estimation based on multi-level dynamic threshold modulation

[0096] 2.2 Estimation in the multi-dynamic threshold adjustment stage. This method accurately estimates non-zero atoms by dynamically adjusting the threshold to ensure the robustness of channel estimation under different signal-to-noise ratio conditions. The specific steps are as follows:

[0097] (1) Initialization: Use the channel matrix output by the first-stage estimation as the input and initialize the multi-level dynamic threshold parameter ;

[0098] (2) Calculate the signal-to-noise ratio: First, calculate the current signal-to-noise ratio based on the current received signal and the noise matrix :

[0099] (8)

[0100] (3) Dynamic threshold adjustment: Calculate the dynamic threshold adjustment factor according to the current signal-to-noise ratio and the target signal-to-noise ratio. The adjustment factor is calculated by the following formula:

[0101] (9)

[0102] In the formula, the subscript "current" represents the current signal-to-noise ratio (SNR), and "target" represents the given target SNR. This factor is used to adjust the dynamic threshold under different SNR conditions to ensure better noise suppression effect under low SNR conditions and improve the channel estimation accuracy under high SNR conditions.

[0103] And dynamically adjust the threshold to adapt to the current channel conditions:

[0104] (10)

[0105] Where is the number of non-zero atoms, is the support set, is the sensing matrix corresponding to the support set; is the variance of the channel noise; the superscript represents the conjugate transpose.

[0106] (4) Precise screening of non-zero atoms: Use the adjusted threshold to further screen the non-zero atoms estimated in the first stage, remove the pseudo non-zero atoms introduced by noise interference, and update the support set and the estimated value;

[0107] (5) Result output: After all iterations are completed, output the final channel estimation matrix .

[0108] By dynamically adjusting the threshold, this method can adaptively adjust the estimation parameters under different SNR conditions to ensure the accuracy and robustness of channel estimation.

[0109] S3 obtains the final channel estimation result through the channel estimation process of the above two stages: the pre-screening strategy stage estimation and the multi-level dynamic threshold adjustment stage estimation, which is expressed as:

[0110] (11)

[0111] Where, is the estimation parameter adjusted according to the dynamic threshold, is the received signal matrix, is the sensing matrix.

[0112] Multi-level dynamic threshold adjustment stage: In the second stage, adopt a dynamic threshold adjustment mechanism to dynamically adjust the reconstruction noise parameter according to the current SNR, and further accurately estimate the non-zero atoms selected in the pre-screening stage; this threshold adjustment mechanism enables the algorithm to adapt to different channel conditions and maintain high robustness in both high SNR and low SNR scenarios. Example 4

[0113] S4 simulation verification is carried out to verify the performance of the estimation method through simulation analysis. The specific performance indicators include the number of atom selections, the normalized mean square error (NMSE), and the reconstruction success probability. The results show that the estimation method of the present invention is superior to traditional algorithms such as OMP and SAMP in terms of performance indicators such as the number of atom selections, the normalized mean square error (NMSE), and the reconstruction success probability, especially showing higher robustness under low signal-to-noise ratio conditions.

[0114] From Figure 1 The results of the comparison diagram of the advantage of the method of the present invention in the number of atom selections show that the pre-screening strategy of the present invention requires fewer atoms for atom selection than the atom selection in the classical hierarchical estimation method. The pre-screening strategy in the present invention effectively filters out unnecessary atoms, improves the accuracy of atom selection, and reduces the computational load. Under low signal-to-noise ratio conditions, the pre-screening strategy in the method of the present invention shows significant advantages in terms of accuracy and convergence speed, ensuring the effectiveness of the overall algorithm. In the algorithm proposed by the present invention, although the added calculation steps slightly increase the complexity, they bring better performance and robustness, especially suitable for noisy environments.

[0115] Figure 2 The comparison diagram of the normalized mean square error and signal-to-noise ratio performance between the method of the present invention and other methods is given. From Figure 2 The normalized mean square error performance analysis in clearly shows that the method of the present invention is always superior to the parallel SAMP, threshold-enhanced hierarchical estimation method, and adaptive threshold-enhanced hierarchical estimation method at all signal-to-noise ratios.

[0116] At a signal-to-noise ratio of 10 dB, the algorithm proposed by the present invention achieves an approximately 30% improvement in the normalized mean square error, a 30% reduction compared to the threshold-enhanced hierarchical estimation method, and a 20% reduction compared to the adaptive threshold-enhanced hierarchical estimation method. This significant improvement is attributed to the pre-screening strategy in the first-stage estimation and the multi-level dynamic threshold adjustment in the second-stage estimation, comprehensively improving the accuracy of non-zero atom estimation and support set identification.

[0117] In the high signal-to-noise ratio range of 20 - 30 dB, the algorithm proposed by the present invention approaches the normalized mean square error performance of the benchmark least squares method. Generally speaking, the method of the present invention shows better normalized mean square error performance, especially at low and medium signal-to-noise ratio levels, verifying the effectiveness and reliability of the estimation method of the present invention in channel estimation in large-scale MIMO systems.

[0118] Figure 3 The comparison diagram of the reconstruction success probability between the method of the present invention and other methods is given. From Figure 3 The results in show that in terms of the reconstruction success probability, the method of the present invention is always superior to the parallel SAMP, threshold-enhanced hierarchical estimation method, and adaptive threshold-enhanced hierarchical estimation method at all signal-to-noise ratios.

[0119] When the signal-to-noise ratio is 10 dB, the algorithm proposed by the present invention achieves a success rate of approximately 80%, while the success rates of the threshold-enhanced hierarchical estimation method and the adaptive threshold-enhanced hierarchical estimation method are 75% and 77% respectively, with increases of 5% and 3% respectively.

[0120] When the signal-to-noise ratio increases to 20 dB, the method of the present invention maintains a success rate of 95%, significantly superior to the threshold-enhanced hierarchical estimation method and the adaptive threshold-enhanced hierarchical estimation method, which are 93% and 94% respectively.

[0121] When the signal-to-noise ratio is 30 dB, the success rate of the method of the present invention is close to 99%, very close to the performance of the benchmark least squares method.

[0122] Overall, the method of the present invention exhibits an excellent success rate in sparse reconstruction, especially at low and medium signal-to-noise ratio levels, confirming its robustness in a noisy environment. The integration of the pre-screening strategy and the multi-level dynamic threshold adjustment enables the method of the present invention to effectively identify and retain important signal components, ensuring accurate channel reconstruction under various signal-to-noise ratio conditions.

[0123] In summary, the present invention combines a pre-screening strategy with a dynamic threshold adjustment mechanism through a hierarchical channel estimation method, which can effectively improve the estimation accuracy and efficiency of spatially non-stationary channels in large-scale MIMO systems, with reduced computational complexity, enhanced adaptability, and improved estimation accuracy. This method can more accurately capture the non-uniform sparse structure in spatially non-stationary channels, significantly improving the channel estimation accuracy, especially with better noise resistance in low signal-to-noise ratio scenarios.

[0124] Based on the above embodiments, the present invention further describes in detail the technical features involved and the functions and roles played by these technical features in the present invention, to help those skilled in the art fully understand the technical solution of the present invention and reproduce it.

[0125] Finally, although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy, characterized in that, The steps are as follows: S1 Establishment of the system model; S2 Hierarchical channel estimation method, which divides the channel estimation process into two stages: pre-screening strategy stage estimation and multi-level dynamic threshold adjustment stage estimation; S2.1 Pre-screening strategy stage estimation. In this estimation stage, first, all possible candidate atoms are preliminarily screened to reduce the computational amount; by calculating the correlation between the candidate atoms and the residual, the candidate atoms most likely to be non-zero are selected to form a primary candidate set; S2.2 Multi-level dynamic threshold adjustment stage estimation. The candidate atom set selected by the pre-screening strategy is further screened, and the accuracy of non-zero atom estimation is improved through a dynamic threshold mechanism; the dynamic threshold is adjusted according to the current signal-to-noise ratio SNR to adaptively handle the estimation accuracy under different channel conditions and ensure the robustness of channel estimation under different SNR conditions; The non-zero atoms are accurately estimated by dynamically adjusting the threshold to ensure the robustness of channel estimation under different SNR conditions. The specific steps are as follows: (1) Initialization: Use the channel matrix estimated and output in the first stage as the input to initialize the multi-level dynamic threshold parameter ; (2)Calculate the signal-to-noise ratio: First, based on the currently received signal and the noise matrix calculate the current signal-to-noise ratio : (8) (3) Dynamic threshold adjustment: Calculate the dynamic threshold adjustment factor based on the current signal-to-noise ratio and the target signal-to-noise ratio , and the adjustment factor is calculated by the following formula: (9) The subscript "current" in the formula represents the current signal-to-noise ratio, and "target" represents the given target signal-to-noise ratio. This factor is used to adjust the dynamic threshold under different SNR conditions to ensure better noise suppression effect under low SNR conditions and improve the channel estimation accuracy under high SNR conditions; And dynamically adjust the threshold to adapt to the current channel conditions: (10) wherein is the number of non-zero atoms, is the support set, is the sensing matrix corresponding to the support set; is the variance of the channel noise; the superscript represents the conjugate transpose; (4) Precise screening of non-zero atoms: Use the adjusted threshold , further screen the non-zero atoms estimated in the first stage, remove the pseudo non-zero atoms introduced by noise interference, and update the support set and the estimated value; (5) Result output: After all iterations are completed, the final channel estimation matrix is output. ; By dynamically adjusting the threshold, the estimation parameters can be adaptively adjusted under different SNR conditions to ensure the accuracy and robustness of channel estimation; S3 Through the channel estimation process in the two stages of pre-screening strategy stage estimation and multi-level dynamic threshold adjustment stage estimation in step S2, the final output channel estimation result is obtained, which is expressed as: (11) Among them, is the estimated parameter adjusted according to the dynamic threshold, is the received signal matrix, is the sensing matrix.

2. The spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy according to claim 1, characterized in that The establishment of the system model in step S1 is specifically as follows: This method is applicable to large-scale MIMO systems. It is assumed that the system uses orthogonal frequency division multiplexing OFDM technology for signal transmission; the base station has a large-scale antenna array and receives signals from multiple users. The channel sparsity is reflected in that some antennas receive effective signals; The received signal is expressed as: (1) Among them, is the received signal matrix, is the sensing matrix, is the channel matrix, is the noise matrix.

3. The spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy according to claim 1, characterized in that In the first-stage estimation based on the pre-screening strategy in the hierarchical channel estimation method in step 2, S2.1 Pre-screening strategy stage estimation. The goal of this process is to reduce the computational complexity in channel estimation and achieve more accurate and efficient channel estimation by screening the potential non-zero atom candidate set. The specific steps are as follows: Initialization: Initialize the channel estimation matrix to be a zero matrix, and the residual matrix to be the received signal matrix , and the initial support set ; (1) Calculate the correlation: Calculate the correlation between each candidate atom and the current residual, and the residual matrix is expressed as: (2) Among them, is the channel estimation value at the -th iteration; (2) Select the primary candidate set: According to the correlation ranking, perform pre-screening and select some atoms with the highest correlation to form a primary candidate set; the correlation is calculated by the following formula: (3) Among them, is the th column of the sensing matrix, is the current residual matrix; (3) Update the candidate support set: The selected primary candidate set is used to update the current support set, and the updated support set is: (4) Among them, is the support set in the previous iteration, is the candidate atom set obtained through pre-screening.

4. The spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and multi-level dynamic threshold strategy according to claim 3, characterized in that It also includes: (4)Channel Estimation Update: Using the updated support set, re-estimate the channel matrix , and update the residual: (5) Among them, is the pseudo-inverse of the sensing matrix corresponding to the support set, is the received signal matrix; (5) Stopping condition: If the preset number of iterations is reached or the residual is less than the set threshold, the iteration ends and the estimated channel matrix is output. .

5. The method for estimating a spatially non-stationary large-scale MIMO channel based on a pre-screening and multi-level dynamic threshold strategy according to claim 3, wherein In step (2), the pre-screening strategy is as follows: according to the atomic correlation ranking, the top atoms are selected to enter the preliminary candidate set; the pre-screening strategy determines the number of selected atoms through a preset ratio ratio, which is set to 10% of the total number of candidate atoms, that is: (6) wherein is the total number of atoms.

6. The method for spatial non-stationary large-scale MIMO channel estimation based on pre-screening and multi-level dynamic threshold strategy according to claim 1, wherein It also includes step S4 Simulation verification. The performance of this estimation method is verified through simulation analysis. The specific performance indicators include the number of atom selections, the normalized mean square error NMSE, and the reconstruction success probability.

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