Enhanced near-field polarization domain channel estimation method based on lazy residual error updating and adaptive weight adjustment mechanism

Abstract

The invention discloses an enhanced near-field polarization domain channel estimation method based on a lazy residual error updating and self-adaptive weight adjustment mechanism, belongs to the technical field of wireless communication, and solves the problem of calculation redundancy under the condition of low SNR (Signal to Noise Ratio) in a traditional method. The method comprises the following steps: constructing a system near-field channel model; an iterative support set is constructed, a lazy residual error updating mechanism controls an updating process through mode detection response and a residual error threshold value, and residual error updating and a channel response vector are executed only when a difference value between a current residual error and a last round residual error exceeds a threshold value, so that invalid matrix operation is avoided; a self-adaptive weight adjustment mechanism dynamically adjusts a weight vector and a residual error threshold value in mode detection according to the updated residual error and a detection mode response value, the detection probability of a main path is enhanced, an interference mode is suppressed, and estimation precision and robustness are improved; through an enhanced algorithm of an over-lazy residual error updating mechanism and an adaptive weight adjustment mechanism, when a residual error is lower than a threshold value or exceeds a maximum iteration value, an estimated channel matrix is output.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to an enhanced near-field polarization domain channel estimation method based on a lazy residual update and an adaptive weight adjustment mechanism. Background Art

[0002] In a near-field XL-MIMO system, since the signal propagates under the condition of a spherical wavefront, the channel exhibits a joint angle-distance non-stationary characteristic. To adapt to such a channel structure, polarization domain channel modeling and estimation have become a research hotspot. Traditional angle domain methods, such as the Orthogonal Matching Pursuit algorithm (OMP) and the Sparse Bayesian Learning algorithm (SBL), are difficult to effectively extract sparse patterns because they do not consider the polarization dimension and the spherical wave characteristics, and their performance drops sharply especially in low signal-to-noise ratio (SNR) scenarios.

[0003] Regarding the near-field effect, the currently authorized patent CN202411351689.2, a method based on multi-candidate bilinear mode detection, effectively improves the channel estimation accuracy by performing mode detection in the angle and distance dual domains and introducing an adaptive weight mechanism. However, the multi-candidate bilinear mode detection method performs residual update and weighted calculation in each round during the iteration process, resulting in a high computational complexity and posing challenges to practical deployment. In addition, this method still has a problem of reduced robustness under low SNR conditions.

[0004] Therefore, there is an urgent need to design a near-field channel estimation method that has low complexity and good low SNR robustness while ensuring high accuracy. Summary of the Invention

[0005] In view of the deficiencies and drawbacks in the prior art, the present invention provides an enhanced near-field polarization domain channel estimation method based on a lazy residual update mechanism and an adaptive weight adjustment mechanism, which effectively reduces the number of matrix multiplications and inverse operations, and at the same time still ensures high robustness and high accuracy under low SNR conditions, and has good scalability.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: The enhanced near-field polarization domain channel estimation method based on a lazy residual update and an adaptive weight adjustment mechanism provided by the present invention includes the following steps:

[0007] S1. Construct a channel model for the near-field region of the system; the channel model is expressed as:

[0008]

[0009] where N is the number of antenna arrays, L is the number of signal paths, g l,m is the path gain, f m is the subcarrier frequency, α(θ l , rl , f m ) represents the array response vector, θ l , r l are the angle and distance of the l-th path respectively;

[0010] S2. Construct an iterative support set, including a lazy residual update mechanism and an adaptive weight adjustment mechanism; the lazy residual update mechanism controls the update process through the pattern detection response and the residual threshold. Only when the difference between the current residual and the previous residual exceeds the threshold, the residual update and the channel response vector are executed. Otherwise, the matrix operation step of the current iteration is skipped to avoid invalid matrix operations;

[0011] The adaptive weight adjustment mechanism dynamically adjusts the weight vector and the residual threshold in pattern detection according to the updated residual and the detected pattern response value, enhances the detection probability of the main path and suppresses the interference pattern, and improves the estimation accuracy and robustness;

[0012] S3. Termination and output: Through the enhanced algorithm of the lazy residual update mechanism and the adaptive weight adjustment mechanism, when the residual is lower than the threshold or exceeds the maximum number of iterations, the estimated channel matrix in the polarization domain is output.

[0013] Preferably, in step S1, the orthogonal frequency division multiplexing (OFDM) technology is used to establish the channel model of the near-field region of the near-field system. The base station is equipped with a large number of antenna arrays, and the signals sent by users propagate in the near-field scenario, and the signals show a sparse structure in the polarization domain;

[0014] The channel propagation characteristics in the near-field polarization domain conform to the spherical wavefront model, and the channel response is constructed according to the spherical wavefront effect. The channel response is specifically expressed as:

[0015]

[0016] Among them, α(θ l , r l , f m ) represents the array response vector of the path of the channel in the polarization domain, c is the speed of light.

[0017] Preferably, in step S2, first perform initialization settings, specifically as follows:

[0018] Set the residual matrix R (0) = Y, where Y is the received signal matrix; the initial support set Γ is an empty set; construct the initial weight matrix W = 1; set the initial residual threshold δ (0) ; and set the threshold decreasing factor γ, γ ∈ (0, 1); set the maximum number of iterations t; based on the above initialization parameters, use the iterative algorithm of the lazy residual update mechanism and the adaptive weight adjustment mechanism to construct the iterative support set.

[0019] Preferably, in step S2, the pattern detection response in the lazy residual update mechanism specifically includes the following steps:

[0020] (1) Calculate the pattern detection response value: According to the projection result of the current residual matrix R (t-1) and the polarization domain joint angle-distance dictionary Φ, calculate the detection response values of all candidate angle-distance patterns. The formula is as follows:

[0021]

[0022] where pattern is the detection response value; Φ H is the polarization domain joint angle-distance dictionary; R (t-1) is the residual matrix; W (t-1) is the weight vector matrix;

[0023] The magnitude of the detection response value of the candidate angle-distance pattern characterizes the probability that the pattern corresponds to the actual signal path; the weight vector W (t-1) is used to enhance the current credible pattern direction;

[0024] (2) Select the maximum response pattern index: Select the maximum joint pattern index mode_idx in the detection response value pattern. This index corresponds to a specific angle-distance combination in the position of the two-dimensional dictionary in the polarization domain, and add it to the support set Γ (t) .

[0025] Preferably, in step S2, the step residual change threshold in the lazy residual update mechanism controls the update process, including the following steps:

[0026] (3) After adding the angle-distance combination corresponding to the position of the maximum joint pattern index selected in step (2) in the two-dimensional dictionary of the polarization domain to the support set Γ (t) , calculate the difference ||R new || F -||R (t-1) || F between the current channel estimation residual and the previous round of channel estimation residual, where ||·|| F represents the Frobenius norm; R new is the current channel estimation residual; R (t-1) is the previous round of channel estimation residual;

[0027] If the difference between the current channel estimation residual and the previous round of channel estimation residual is less than the preset residual threshold δ (t) , at this time, the channel estimation has not been effectively improved, then skip the current round of residual update and channel estimation;

[0028] If the difference between the current channel estimation residual and the previous round of channel estimation residual is greater than the preset residual threshold δ (t) and at this time, the channel estimation effect is significantly improved, then the channel response vector and the residual need to be updated.

[0029] Preferably, the update of the channel response vector and the residual is specifically as follows: for the patterns within the support set, the least squares estimation algorithm is adopted to update the channel response vector and the residual, and the formula is as follows:

[0030]

[0031]

[0032] where is the updated channel response vector; R (t) is the updated channel residual; Φ Γ represents the sub-dictionary corresponding to the support set, the superscript H represents taking the transpose, y represents the received signal vector, and Y represents the received signal matrix.

[0033] Preferably, in step S2, the weight vector in the dynamic adjustment mode detection of the adaptive weight adjustment mechanism specifically includes the following steps:

[0034] (4) According to the updated residual distribution and the detected response pattern value in step (3), considering factors such as the contribution of each pattern to the residual and the energy intensity, dynamically update the weight vector W (t) , in order to enhance the main path detection rate and suppress the interference pattern, the function formula used to update the weight vector is as follows:

[0035] W (t) ←f(R (t) ,pattern (t) )

[0036] where R (t) is the updated channel residual, pattern (t) is the detected response value, and W (t) is the updated weight vector.

[0037] Preferably, it also includes dynamically adjusting the updated residual threshold, and adopting a decreasing residual update threshold during the iteration process to promote the convergence of the algorithm. The specific calculation formula is: δ (t+1) =γ·δ (t) , where γ∈(0,1).

[0038] Preferably, step S3 includes a termination judgment:

[0039] If the current round of channel residual is already lower than the set threshold δ (t)Otherwise, if the number of iterations has exceeded the maximum setting, output the estimated channel response matrix in the near-field polarization domain

[0040] The present invention provides an enhanced near-field polarization domain channel estimation method based on a lazy residual update and an adaptive weight adjustment mechanism, which has the following beneficial effects:

[0041] (1) The enhanced near-field polarization domain channel estimation method of the present invention based on a lazy residual update and an adaptive weight adjustment mechanism combines a lazy residual update mechanism and an adaptive weight adjustment mechanism, enabling it to update the residual and the support set only when the estimation performance is effectively improved, avoiding invalid matrix operations; by dynamically adjusting the weight vector setting in pattern detection to dynamically adjust the residual change threshold, suppressing the problem of error detection propagation caused by noise interference under low signal-to-noise ratio (SNR); enhancing the detection probability of the main path and suppressing interference patterns, significantly improving the robustness of channel estimation.

[0042] Meanwhile, the enhanced algorithm of the present invention based on a lazy residual update mechanism and an adaptive weight adjustment mechanism has excellent computational efficiency. By reducing the frequent update operations of the residual and the support set, the number of matrix multiplications and matrix inverse operations is significantly reduced. Compared with the multi-candidate bilinear pattern detection algorithm, under the condition of the same number of paths, the number of residual updates can be reduced by about 30%, and a more stable weight update process can be achieved.

[0043] (2) The algorithm architecture of the present invention based on a lazy residual update mechanism and an adaptive weight adjustment mechanism exhibits good scalability and can be compatible with larger-scale array configurations and broadband communication scenarios. This design gives it significant advantages in the actual deployment of modern communication systems such as 5G / 6G, and can adapt to the trend requirements of future communication technologies towards large-scale MIMO and broadbandization. Brief Description of the Drawings

[0044] Figure 1 is a schematic diagram of the near-field polarization domain channel propagation model in the present invention;

[0045] Figure 2 is a performance comparison curve of the normalized mean square error (NMSE) between the present invention and other different channel estimation algorithms under different signal-to-noise ratio (SNR) conditions;

[0046] Figure 3 is a performance comparison curve of the normalized mean square error (NMSE) between the present invention and other different channel estimation algorithms when increasing the number of base station antennas; Detailed Embodiments

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 the embodiments.

[0048] Embodiment 1

[0049] An enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism includes the following steps:

[0050] S1. Construct a near-field polarization domain channel model and adopt the following simulation environment: the number of base station antenna arrays: N = 256 (uniform linear array); the number of users: K = 4, adopt OFDM technology, the number of subcarriers is M = 256, the bandwidth B = 10 GHz, and the center frequency f c = 28 GHz; the number of signal paths: L = 6, and each path has different angular domain distance parameters;

[0051] An orthogonal frequency division multiplexing (OFDM) technology is used to establish a channel model in the near-field region of the near-field system. The base station is equipped with a large number of antenna arrays, and the signals sent by users propagate in the near-field scenario, and the signals show a sparse structure in the polarization domain; the channel model is expressed as:

[0052]

[0053] where N is the number of antenna arrays, L is the number of signal paths, g l,m is the path gain, f m is the subcarrier frequency, α(θ l , r l , f m ) represents the array response vector, and θ l , r l are the angle and distance of the l-th path respectively.

[0054] The channel propagation characteristics in the near-field polarization domain conform to the spherical wavefront model. According to the spherical wavefront effect, the channel response is constructed. As Figure 1 shown, in the near-field channel model, the array response vector α(θ l , r l , f m ) is defined as where α(θ l , r l , f m ) represents the array response vector of the path of the channel in the polarization domain, c is the speed of light.

[0055] And r l (n) is the distance from the last hop of scattering to the n-th base station antenna. Based on the geometric structure of the base station array, rl (n) can be approximated as and δ n = n - (N - 1) / 2, n ∈ {0, 1, …, N - 1}, d = λ c / 2 is the antenna spacing, and λ c = c / f c is the carrier wavelength.

[0056] S2. Construct an iterative support set. First, perform initialization settings. Set the residual matrix R (0) = Y, where Y is the received signal matrix; the initial support set Γ is an empty set; construct the initial weight matrix W = 1; set the initial residual threshold δ (0) ; and set the threshold decay factor γ, γ ∈ (0, 1); set the maximum number of iterations t. Based on the above initialization parameters, use an iterative algorithm with a lazy residual update mechanism and an adaptive weight adjustment mechanism to construct the iterative support set.

[0057] The lazy residual update mechanism controls the update process through the pattern detection response and the residual threshold. Only when the difference between the current residual and the previous residual exceeds the threshold, perform residual update and channel response vector calculation; otherwise, skip the matrix operation steps of the current iteration to avoid invalid matrix operations; the adaptive weight adjustment mechanism dynamically adjusts the weight vector and residual threshold in pattern detection according to the updated residual and the detected pattern response value, enhances the detection probability of the main path and suppresses interference patterns, and improves the estimation accuracy and robustness. The algorithm iteration process is as follows:

[0058] (1) Calculate the pattern detection response value: According to the projection result of the current residual matrix R (t-1) and the polarization domain joint angle - distance dictionary Φ, calculate the detection response values of all candidate angle - distance patterns. The formula is as follows:

[0059] pattern (t) = W (t-1) ·|Φ H ·R (t-1) |

[0060] where pattern is the detection response value; Φ H is the polarization domain joint angle - distance dictionary; R (t-1) is the residual matrix; W (t-1) is the weight vector matrix;

[0061] The magnitude of the detection response value of the candidate angle - distance pattern characterizes the probability that the pattern corresponds to the actual signal path; the weight vector W (t-1) is used to enhance the current credible pattern direction.

[0062] (2) Select the maximum response mode index: Select the maximum combined mode index mode_idx in the detection response value pattern. This index corresponds to a specific angle - distance combination in the position of the two - dimensional dictionary in the polarization domain, and add it to the support set Γ (t) .

[0063] (3) Residual change threshold controls the update process: After adding the angle - distance combination corresponding to the position of the maximum combined mode index selected in step (2) in the two - dimensional dictionary in the polarization domain to the support set, calculate the difference between the current channel estimation residual and the previous round of channel estimation residual, where represents the Frobenius norm; is the current channel estimation residual; is the previous round of channel estimation residual;

[0064] If the difference between the current channel estimation residual and the previous round of channel estimation residual is less than the preset residual threshold, at this time the channel estimation has not been effectively improved, then skip the current round of residual update and channel estimation;

[0065] If the difference between the current channel estimation residual and the previous round of channel estimation residual is greater than the preset residual threshold, at this time the channel estimation effect has been significantly improved, then use the least - squares estimation for the modes in the support set to update the channel response vector and the residual. The formulas are as follows:

[0066]

[0067]

[0068] Where, is the updated channel response vector; R (t) is the updated channel residual; Φ Γ represents the sub - dictionary corresponding to the support set, the superscript H represents taking the transpose, y represents the received signal vector, and Y represents the received signal matrix.

[0069] (4) Dynamically adjust the weight vector in mode detection: According to the updated residual distribution and detection response pattern values in step (3), considering factors such as the contribution of each mode to the residual and the energy intensity, dynamically update the weight vector to enhance the main - path detection rate and suppress interference modes. The function formula for updating the weight vector is as follows::

[0070] W (t) ←f(R (t) , pattern (t) )

[0071] Where, R (t) is the updated channel residual, pattern (t) is the detection response value, and W (t) is the updated weight vector.

[0072] (5) Update residual threshold: Dynamically adjust the update residual threshold, and adopt a decreasing residual update threshold during the iteration process to promote the convergence of the algorithm. The specific calculation formula is as follows: specifically, δ (t+1) = γ·δ (t) , where γ ∈ (0, 1).

[0073] S3 Termination and output: The enhanced algorithm with the lazy residual update mechanism and the adaptive weight adjustment mechanism outputs the estimated channel matrix in the near-field polarization domain. If the current round of channel residual is lower than the set threshold δ (t) , or the number of iterations has exceeded the maximum setting, then output the estimated channel response matrix in the near-field polarization domain

[0074] Embodiment 2

[0075] Verify and compare the normalized mean square error (NMSE) performance of the enhanced near-field polarization domain channel estimation method based on the lazy residual update and the adaptive weight adjustment mechanism of the present invention with other existing estimation methods (such as angular domain OMP, polarization domain OMP, beam splitting mode detection, bilinear mode detection, multi-candidate bilinear mode detection) under different SNR conditions. Use Matlab simulation, and the specific results and analysis are as follows:

[0076] Figure 2 is the comparison curve graph of the normalized mean square error (NMSE) performance of the present invention and different channel estimation algorithms under different signal-to-noise ratios SNR conditions; as Figure 2 shown, the system bandwidth is set to 10 GHz, the farthest distance is 20 meters, the nearest distance is 10 meters, and the number of antennas is set to 256. The normalized mean square error performance of the method of the present invention under different γ values (γ = 0.1, γ = 0.5, γ = 0.9) is compared. Compared with traditional algorithms (such as angular domain OMP, polarization domain OMP, beam splitting mode detection, bilinear mode detection, multi-candidate bilinear mode detection), the method of the present invention always achieves the lowest NMSE value in a low SNR environment. Especially when the signal-to-noise ratio SNR = -5 dB, the NMSE value of the method of the present invention is about 2 dB lower than that of the multi-candidate bilinear mode detection method and about 6 dB lower than that of the polarization domain OMP; and it is significantly better than the other remaining comparison methods, highlighting the effectiveness of the adaptive residual update mechanism controlled by γ of the method of the present invention.

[0077] And from Figure 2It can also be seen from the simulation results that different γ values in the method of the present invention have a certain influence on the NMSE and SNR performance of the algorithm. Under low SNR conditions (-5 dB to 0 dB), the performances of the three γ settings are relatively similar, and the performance differences among them are small. However, the setting of γ = 0.5 has a lower NMSE value compared to the conditions of γ = 0.1 and γ = 0.9. There is only a marginal improvement in the estimation accuracy of γ = 0.5, which indicates that setting γ = 0.5 achieves a better balance between the residual update frequency and the estimation accuracy, although the advantage under low SNR is limited.

[0078] Figure 3 This is a comparison curve graph of the normalized mean square error (NMSE) performance of the present invention and different channel estimation algorithms when increasing the number of base station antennas. As Figure 3 shown, in terms of system settings, the system bandwidth is set to 10 GHz, the maximum distance is 20 meters, the minimum distance is 10 meters, the number of antennas is set to 512, and γ = 0.5. Under the condition of 512 antennas, the estimation accuracy of the method of the present invention remains stable, showing excellent broadband adaptability. And in the medium and low SNR regions (-5 dB to 0 dB), it performs better than the multi-candidate bilinear mode detection method, and has an advantage of about 2 - 3 dB compared to the multi-candidate bilinear mode detection method. This shows that the enhanced algorithm in the present invention through the lazy residual update mechanism and the adaptive weight adjustment mechanism can make full use of the higher spatial resolution brought by the larger-scale antenna array, thereby improving the accuracy of channel estimation.

[0079] As Figure 2 and Figure 3 shown, the method of the present invention has robust performance under different antenna configurations, especially under low SNR conditions. When the number of antennas is large, the method of the present invention is significantly better than the multi-candidate bilinear mode detection method. And under the same conditions, the average running time of the method of the present invention is 72.6% of that of the multi-candidate bilinear mode detection method, and the number of iterations is reduced by 30%. This verifies the effectiveness of the lazy residual update mechanism and the adaptive weighted adjustment mechanism in improving the calculation efficiency and estimation accuracy, especially applicable to the challenging scenario of low SNR.

[0080] In summary, for the enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism of the present invention, lazy residual update avoids the propagation of error detection, significantly improves the estimation robustness, avoids frequent update of residuals and support sets, effectively reduces the number of matrix multiplications and inverse operations. Compared with the multi-candidate bilinear pattern detection algorithm, the present invention reduces the number of residual updates by about 30% under the condition of the same number of paths, and the weight update is more stable. At the same time, the method of the present invention also exhibits good scalability and can be compatible with larger-scale array configurations and broadband communication scenarios. This design gives it significant advantages in the actual deployment of modern communication systems such as 5G / 6G, and can adapt to the trend requirements of future communication technologies towards large-scale MIMO and broadband development.

[0081] On the basis of the above embodiments, the present invention continues to describe in detail the technical features involved and the functions and roles played by these technical features in the present invention, so as to help those skilled in the art fully understand the technical solution of the present invention and reproduce it.

[0082] 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. An enhanced near-field polarization domain channel estimation method based on a lazy residual update and an adaptive weight adjustment mechanism, characterized in that, The steps are as follows: S1. Construct a channel model for the near-field region of the system; the channel model is expressed as: where N is the number of antenna arrays, L is the number of signal path, and g l,m is the path gain, f m is the subcarrier frequency, α(θ l , r l , f m ) represents the array response vector, θ l , r l are the angle and distance of the l-th path respectively; S2. Construct an iterative support set, including a lazy residual update mechanism and an adaptive weight adjustment mechanism; the lazy residual update mechanism controls the update process through the pattern detection response and the residual threshold, and only when the difference between the current residual and the previous residual exceeds the threshold, the residual update and the channel response vector are executed, otherwise the matrix operation steps of the current iteration are skipped to avoid invalid matrix operations; The adaptive weight adjustment mechanism dynamically adjusts the weight vector and the residual threshold in pattern detection according to the updated residual and the detected pattern response value, enhances the detection probability of the main path and suppresses the interference pattern, and improves the estimation accuracy and robustness; S3. Termination and output: Through the enhanced algorithm of the lazy residual update mechanism and the adaptive weight adjustment mechanism, when the residual is lower than the threshold or exceeds the maximum number of iterations, the estimated channel matrix in the polarization domain is output.

2. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that In step S1, the orthogonal frequency division multiplexing (OFDM) technology is adopted to establish a channel model for the near-field region of the near-field system. The base station is equipped with a large number of antenna arrays, and the signals sent by users propagate in the near-field scenario, and the signals are represented as a sparse structure in the polarization domain; The channel propagation characteristics in the near-field polarization domain conform to the spherical wavefront model, and the channel response is constructed according to the spherical wavefront effect. The channel response is specifically expressed as: Among them, α(θ l , r l , f m ) represents the array response vector of the channel path in the polarization domain, c is the speed of light.

3. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 1, wherein, In step S2, first, the initialization settings are as follows: Set the residual matrix R (0) = Y, where Y is the received signal matrix; the initial support set Γ is an empty set; Construct the initial weight matrix \(W = 1\); set the initial residual threshold \(\delta\). (0) ; and set the threshold decreasing factor \(\gamma\), where \(\gamma\in(0, 1)\); set the maximum number of iterations \(t\); based on the above initialization parameters, use an iterative algorithm with a lazy residual update mechanism and an adaptive weight adjustment mechanism to construct an iterative support set.

4. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 3, characterized in that In step S2, the pattern detection response in the lazy residual update mechanism specifically includes the following steps: (1) Calculate the detection response value of the pattern: According to the current residual matrix R (t-1) and the projection result of the polarization domain joint angle-distance dictionary Φ, calculate the detection response values of all candidate angle-distance patterns. The formula is as follows: pattern (t) = W (t-1) ·|Φ H ·R (t-1) | Among them, pattern is the detection response value; Φ H is the polarization domain joint angle-distance dictionary; R (t-1) is the residual matrix; W (t-1) is the weight vector matrix; The magnitude of the detection response value of the candidate angle-distance pattern characterizes the probability that the pattern corresponds to the actual signal path; weight vector W (t-1) used to enhance the direction of the current credible pattern; (2) Select the maximum response mode index: Select the maximum combined mode index mode_idx in the detection response value pattern. This index corresponds to a specific angle-distance combination in the position of the two-dimensional dictionary in the polarization domain, and add it to the support set Γ (t) .

5. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 4, characterized in that In step S2, the residual change threshold in the lazy residual update mechanism controls the update process, including the following steps: (3) Add the angle - distance combination corresponding to the position of the maximum joint pattern index selected in step (2) in the two - dimensional dictionary of the polarization domain to the support set Γ (t) After that, calculate the difference ||R new || F -||R (t-1) || F between the current channel estimation residual and the previous - round channel estimation residual, where ||·|| F represents the Frobenius norm; R new is the current channel estimation residual; R (t-1) is the previous - round channel estimation residual; If the difference between the current channel estimation residual and the previous round of channel estimation residual is less than the preset residual threshold δ (t) at this time, if the channel estimation has not been effectively improved, then skip the residual update and channel estimation in this round; If the difference between the current channel estimation residual and the previous round of channel estimation residual is greater than the preset residual threshold δ (t) At this time, if the channel estimation effect is significantly improved, the channel response vector and the residual need to be updated.

6. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 5, characterized in that The update of the channel response vector and the residual is specifically to adopt the least squares estimation algorithm for the patterns in the support set to update the channel response vector and the residual. The formula is as follows: Among them, is the updated channel response vector; R (t) is the updated channel residual; Φ Γ represents the sub-dictionary corresponding to the support set, the superscript H represents taking the transpose, and y represents the received signal vector.

7. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 5, characterized in that In step S2, the dynamic adjustment of the weight vector in pattern detection in the adaptive weight adjustment mechanism specifically includes the following steps: (4) According to the updated residual distribution and the detected response pattern values in step (3), dynamically update the weight vector W based on factors such as the contribution of each pattern to the residual and the energy intensity (t) , so as to enhance the main path detection rate and suppress interference patterns. The function formula for updating the weight vector is as follows: W (t) ←f(R (t) , pattern (t) ) Among them, R (t) is the updated channel residual, and pattern (t) is the detection response value, and W (t) is the updated weight vector.

8. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 7, wherein It also includes dynamically adjusting and updating the residual threshold, and adopting a decreasing residual update threshold during the iteration process to promote the convergence of the algorithm. The specific calculation formula is: δ (t+1) = γ·δ (t) , where γ ∈ (0, 1).

9. The enhanced near-field polarization domain channel estimation method based on the lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, Step S3 includes a termination determination: If the current round of channel residuals is already lower than the set threshold δ (t) , or the number of iterations has exceeded the maximum setting, then output the channel estimation matrix response in the near-field polarization domain

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