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

Abstract

The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism of the application belongs to the technical field of wireless communication, and solves the problem of redundant calculation of the traditional method under low SNR conditions. The application comprises constructing a system near-field channel model; constructing an iterative support set; a lazy residual update mechanism controls the update process through mode detection response and residual threshold threshold value, and only when the difference between the current residual and the last round residual exceeds the threshold value, residual update and channel response vector are executed, avoiding invalid matrix operation; an adaptive weight adjustment mechanism dynamically adjusts the weight vector in mode detection and the residual threshold threshold value according to the updated residual and the detection mode response value, enhances the detection probability of the main path and suppresses the interference mode, improves the estimation accuracy and robustness; through the enhanced algorithm of the lazy residual update mechanism and the adaptive weight adjustment mechanism, the estimated channel matrix is output when the residual is lower than the threshold value or exceeds the maximum iteration value.

Description

Technical Field

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

[0002] In near-field XL-MIMO systems, the propagation of signals under spherical wavefront conditions leads to joint angle-range nonstationary characteristics in the channel. To adapt to this 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), fail to effectively extract sparse patterns due to their lack of consideration for polarization dimension and spherical wavefront characteristics, especially exhibiting a sharp performance degradation in low signal-to-noise ratio (SNR) scenarios.

[0003] To address the near-field effect, the currently authorized patent CN202411351689.2 describes a method based on multi-candidate bilinear pattern detection. This method effectively improves channel estimation accuracy by performing pattern detection in both angle and range domains and introducing an adaptive weighting mechanism. However, the multi-candidate bilinear pattern detection method involves residual updates and weighted calculations in each iteration, resulting in high computational complexity and posing challenges for practical deployment. Furthermore, this method still suffers from decreased robustness under low SNR conditions.

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

[0005] To address the shortcomings and deficiencies of existing technologies, this invention provides an enhanced near-field polarization domain channel estimation method based on a lazy residual update mechanism and an adaptive weight adjustment mechanism. This method effectively reduces the number of matrix multiplications and inverse operations, while maintaining high robustness and accuracy under low SNR conditions, and also exhibits good scalability.

[0006] To achieve the above objectives, the present invention provides an enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism, comprising the following steps:

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

[0008]

[0009] in The number of antenna arrays, Number of channel paths For path gain, For subcarrier frequency, Represents the array response vector. , The first The angle and distance of the path;

[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 residual threshold. Only when the difference between the current residual and the previous residual exceeds the threshold is the residual update and channel response vector executed. Otherwise, the matrix operation steps of the current iteration are skipped to avoid invalid matrix operations.

[0011] The adaptive weight adjustment mechanism dynamically adjusts the weight vector and residual threshold in pattern detection based on the updated residual and detection pattern response value, thereby enhancing the detection probability of the main path and suppressing interference patterns, thus improving estimation accuracy and robustness.

[0012] S3. Termination and Output: The enhanced algorithm, which uses a lazy residual update mechanism and an adaptive weight adjustment mechanism, outputs the estimated channel matrix in the polarization domain when the residual is below the threshold or exceeds the maximum iteration value.

[0013] Preferably, in step S1, the channel model of the near-field region of the near-field system is established using orthogonal frequency division multiplexing (OFDM) technology. The base station is equipped with a large number of antenna arrays. The signal emitted by the user propagates in the near-field scenario, and the signal is characterized by a sparse structure in the polarization domain.

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

[0015]

[0016] in, The array response vector representing the path of the channel in the polarization domain. c is the speed of light.

[0017] Preferably, in step S2, initialization settings are first performed, as follows:

[0018] Set the residual matrix ,in For the received signal matrix; initial support set For an empty set; construct the initial weight matrix. Set initial residual threshold And set a threshold decrease factor. Set the maximum number of iterations t; based on the above initialization parameters, construct the iterative support set using an iterative algorithm with a lazy residual update mechanism and an adaptive weight adjustment mechanism.

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

[0020] (1) Calculate the mode detection response value: based on the current residual matrix Joint angle-distance dictionary with polarization domain Based on the projection results, the detection response values ​​of all candidate angle-distance patterns are calculated using the following formula:

[0021]

[0022] in, To detect the response value; For the joint angle-distance dictionary of the polarization domain; The residual matrix; The weight vector matrix;

[0023] The magnitude of the detection response value of the candidate angle-distance pattern represents the probability that the pattern corresponds to the actual channel path; the weight vector Used to enhance the current trusted mode direction;

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

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

[0026] (3) Add the angle-distance combination corresponding to the position of the largest joint mode index selected in step (2) in the polarization domain two-dimensional dictionary to the support set. Then, calculate the difference between the current channel estimation residual and the channel estimation residual from the previous round. ,in, Denotes the Frobenius norm; Estimate the residual for the current channel; The residual from the previous round of channel estimation;

[0027] If the difference between the current channel estimation residual and the previous channel estimation residual is less than a preset residual threshold If the channel estimation has not been effectively improved at this point, then skip the current round of residual update and channel estimation.

[0028] If the difference between the current channel estimation residual and the previous channel estimation residual is greater than a preset residual threshold... If the channel estimation performance is significantly improved at this point, then the channel response vector and residual need to be updated.

[0029] Preferably, updating the channel response vector and residuals involves using a least squares estimation algorithm for the modes within the support set to update the channel response vector and residuals, using the following formula:

[0030]

[0031]

[0032] in, To update the channel response vector; This is the updated channel residual; Indicates the sub-dictionary corresponding to the support set, superscript This indicates the transpose. Represents the received signal vector. This 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) Based on the updated residual distribution and detection response pattern value in step (3), dynamically update the weight vector, taking into account factors such as the contribution of each mode to the residual and energy intensity. To enhance the main path detection rate and suppress interfering patterns, the function formula used to update the weight vector is as follows:

[0035]

[0036] in, For the updated channel residual, To detect the response value, This is the updated weight vector.

[0037] Preferably, the algorithm also includes dynamically adjusting the residual update threshold, using a decreasing residual update threshold during iteration to promote algorithm convergence. The specific calculation formula is as follows: ,in .

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

[0039] If the channel residual in this round is lower than the set threshold If the number of iterations has exceeded the maximum setting, then output the estimated channel response matrix in the near-field polarization domain. .

[0040] This invention provides an enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism. It has the following advantages:

[0041] (1) The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism of the present invention combines the lazy residual update mechanism and the adaptive weight adjustment mechanism, so that the residual and support set are updated only when the estimation performance is effectively improved, thus avoiding invalid matrix operations; by setting the threshold of residual change in dynamic adjustment of weight vector in dynamic mode detection, the problem of false detection propagation caused by noise interference under low signal-to-noise ratio (SNR) is suppressed; the detection probability of the main path is enhanced and the interference mode is suppressed, thus significantly improving the robustness of channel estimation.

[0042] Meanwhile, the enhanced algorithm of this invention, based on a lazy residual update mechanism and an adaptive weight adjustment mechanism, exhibits excellent computational efficiency. By reducing the frequent update operations between the residual and the support set, it significantly reduces the number of matrix multiplications and matrix inversions. Compared with the multi-candidate bilinear pattern detection algorithm, it can reduce the number of residual updates by approximately 30% under the same number of paths and achieve a more stable weight update process.

[0043] (2) The present invention exhibits good scalability through an algorithm architecture based on a lazy residual update mechanism and an adaptive weight adjustment mechanism, and is 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 future trend of communication technology developing towards large-scale MIMO and broadband. Attached Figure Description

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

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

[0046] Figure 3 This is a comparison curve of the normalized mean square error (NMSE) performance of the present invention with other different channel estimation algorithms under the condition of increasing the number of antennas at the base station. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1

[0048] The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism includes the following steps:

[0049] S1. Construct a near-field polarization domain channel model using the following simulation environment: Number of base station antenna arrays: N=256 (uniform linear array); Number of users: K=4; OFDM technology is used; Number of subcarriers: M=256; Bandwidth: B=10GHz; Center frequency: Number of channel paths: Each path has a different angular distance parameter;

[0050] The channel model for the near-field region of the near-field system is established using Orthogonal Frequency Division Multiplexing (OFDM) technology. The base station is equipped with a large number of antenna arrays. The signals emitted by the user propagate in the near-field scenario, exhibiting a sparse structure in the polarization domain. The channel model is represented as follows:

[0051]

[0052] in The number of antenna arrays, Number of channel paths Path gain For subcarrier frequency, Represents the array response vector. , The first The angle and distance of the path.

[0053] The channel propagation characteristics in the near-field polarization domain conform to the spherical wavefront model. The channel response is constructed based on the spherical wavefront effect, such as... Figure 1 As shown, in the near-field channel model, the array response vector Defined as ,in, The array response vector representing the path of the channel in the polarization domain. c is the speed of light.

[0054] and From the last scattering to the first The distance between each base station antenna. Based on the geometry of the base station array, It can be approximated as ,and It is the antenna spacing. It is the carrier wavelength.

[0055] S2. Construct the iterative support set, first initialize the settings, and set the residual matrix. ,in For the received signal matrix; initial support set For an empty set; construct the initial weight matrix. Set initial residual threshold And set a threshold decrease factor. Set the maximum number of iterations t. Based on the above initialization parameters, an iterative support set is constructed using an iterative algorithm with a lazy residual update mechanism and an adaptive weight adjustment mechanism.

[0056] The lazy residual update mechanism controls the update process through the pattern detection response and residual threshold. Only when the difference between the current residual and the previous residual exceeds the threshold is the residual update and channel response vector executed; otherwise, the matrix operation step of the current iteration is skipped to avoid invalid matrix operations. The adaptive weight adjustment mechanism dynamically adjusts the weight vector and residual threshold in pattern detection based on the updated residual and the detected pattern response value, enhancing the detection probability of the main path and suppressing interfering patterns, thereby improving estimation accuracy and robustness. The algorithm iteration process is as follows:

[0057] (1) Calculate the mode detection response value: based on the current residual matrix Joint angle-distance dictionary with polarization domain Based on the projection results, the detection response values ​​of all candidate angle-distance patterns are calculated using the following formula:

[0058]

[0059] in, To detect the response value; For the joint angle-distance dictionary of the polarization domain; The residual matrix; The weight vector matrix;

[0060] The magnitude of the detection response value of the candidate angle-distance pattern represents the probability that the pattern corresponds to the actual channel path; the weight vector Used to enhance the current trusted mode direction.

[0061] (2) Select the maximum response mode index: Select the joint mode index mode_idx with the largest detection response value pattern. This index corresponds to a specific angle-distance combination in the polarization domain two-dimensional dictionary and add it to the support set. .

[0062] (3) Residual change threshold control update process: After adding the angle-distance combination corresponding to the position of the largest joint mode index selected in step (2) in the polarization domain two-dimensional dictionary 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; and is the previous round of channel estimation residual;

[0063] If the difference between the current channel estimation residual and the channel estimation residual of the previous round is less than the preset residual threshold, then the channel estimation has not been effectively improved, and the current round of residual update and channel estimation is skipped.

[0064] If the difference between the current channel estimation residual and the previous channel estimation residual is greater than a preset residual threshold, then the channel estimation effect has significantly improved. In this case, least squares estimation is applied to the modes within the support set to update the channel response vector and residuals. The formula is as follows:

[0065]

[0066]

[0067] in, To update the channel response vector; This is the updated channel residual; Indicates the sub-dictionary corresponding to the support set, superscript This indicates the transpose. Represents the received signal vector. This represents the received signal matrix.

[0068] (4) Dynamically adjust the weight vector in pattern detection: Based on the residual distribution and detection response pattern value updated in step (3), and considering factors such as the contribution of each pattern to the residual and energy intensity, dynamically update the weight vector to enhance the main path detection rate and suppress interfering patterns. The function formula used to update the weight vector is as follows:

[0069]

[0070] in, For the updated channel residual, To detect the response value, This is the updated weight vector.

[0071] (5) Update residual threshold: Dynamically adjust the residual update threshold. During the iteration process, a decreasing residual update threshold is used to promote algorithm convergence. The specific calculation formula is as follows: ,in .

[0072] S3 Termination and Output: An enhanced algorithm using a lazy residual update mechanism and an adaptive weight adjustment mechanism outputs the estimated channel matrix in the near-field polarization domain. If the channel residual in this round is below a set threshold... If the number of iterations has exceeded the maximum setting, then output the estimated channel response matrix in the near-field polarization domain. . Example 2

[0073] The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism of this invention is compared with the normalized mean square error (NMSE) performance of other existing estimation methods (such as angle-domain OMP, polarization-domain OMP, beam splitting mode detection, bilinear mode detection, and multi-candidate bilinear mode detection) under different SNR conditions. Matlab simulations were used, and the specific results and analysis are as follows:

[0074] Figure 2 This is a comparison curve of the normalized mean square error (NMSE) performance of the present invention and different channel estimation algorithms under different signal-to-noise ratio (SNR) conditions; as shown. Figure 2 As shown, the system bandwidth is set to 10 GHz, the maximum distance is 20 meters, the minimum distance is 10 meters, and the number of antennas is set to 256. The normalized mean square error (NMSE) performance of the method of this invention is compared under different γ values ​​(γ=0.1, γ=0.5, γ=0.9). Compared with traditional algorithms (such as angular domain OMP, polarization domain OMP, beam splitting mode detection, bilinear mode detection, and multi-candidate bilinear mode detection), the method of this invention consistently achieves the lowest NMSE value in low SNR environments. Especially at a signal-to-noise ratio (SNR) of -5 dB, the NMSE value of the method of this invention is approximately 2 dB lower than that of the multi-candidate bilinear mode detection method and approximately 6 dB lower than that of polarization domain OMP; furthermore, it significantly outperforms the remaining comparative methods, highlighting the effectiveness of the adaptive residual update mechanism controlled by γ in this invention.

[0075] And from Figure 2 Simulation results also show that different γ values ​​in the method of this invention have a certain impact on the NMSE and SNR performance of the algorithm. Under low SNR conditions (-5 dB to 0 dB), the three γ settings perform similarly, with little difference in performance; however, the NMSE value of the γ=0.5 setting is lower than that of the γ=0.1 and γ=0.9 settings, and γ=0.5 only provides a marginal improvement in estimation accuracy. This indicates that setting γ=0.5 achieves a good balance between residual update frequency and estimation accuracy, although the advantage is limited under low SNR conditions.

[0076] Figure 3 This is a comparison curve of the normalized mean square error (NMSE) performance of the present invention and different channel estimation algorithms under the condition of increasing the number of antennas at the base station. Figure 3As shown, the system settings are: system bandwidth of 10 GHz, maximum distance of 20 meters, minimum distance of 10 meters, number of antennas of 512, and γ = 0.5. The method of this invention maintains stable estimation accuracy even with 512 antennas, demonstrating excellent broadband adaptability. Furthermore, it outperforms the multi-candidate bilinear pattern detection method in the low to medium SNR region (-5 dB to 0 dB), exhibiting an advantage of approximately 2–3 dB. This indicates that the enhanced algorithm in this invention, employing a lazy residual update mechanism and an adaptive weight adjustment mechanism, can fully utilize the higher spatial resolution provided by a larger antenna array, thereby improving the accuracy of channel estimation.

[0077] like Figure 2 and Figure 3 As shown, the method of this invention exhibits robust performance under different antenna configurations, especially under low SNR conditions. Furthermore, when the number of antennas is large, the method of this invention significantly outperforms the multi-candidate bilinear pattern detection method. Under the same conditions, the average runtime of the method of this invention is 72.6% of that of the multi-candidate bilinear pattern 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 computational efficiency and estimation accuracy, and is particularly suitable for the challenging scenario of low SNR.

[0078] In summary, the enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism of this invention avoids the propagation of false detections through lazy residual update, significantly improving estimation robustness. It also avoids frequent updates to residuals and support sets, effectively reducing the number of matrix multiplications and inverse operations. Compared with multi-candidate bilinear pattern detection algorithms, this invention reduces the number of residual updates by approximately 30% under the same path count conditions, and the weight updates are more stable. Furthermore, this method exhibits good scalability, compatible with larger-scale array configurations and broadband communication scenarios. This design gives it significant advantages in the practical deployment of modern communication systems such as 5G / 6G, and it can adapt to the future trend of communication technologies moving towards large-scale MIMO and broadband.

[0079] Based on the above embodiments, the present invention continues to describe in detail the technical features involved therein and the functions and roles of these technical features in the present invention, so as to help those skilled in the art to fully understand the technical solution of the present invention and reproduce it.

[0080] Finally, although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. 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 lazy residual update and adaptive weight adjustment mechanism, characterized in that, The steps include the following: S1. Construct the channel model for the near-field region of the system; the channel model is represented as: ； in The number of antenna arrays, Number of channel paths For path gain, For subcarrier frequency, Represents the array response vector. , The first The angle and distance of the path; 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 mode detection response and residual threshold. Only when the difference between the current residual and the previous residual exceeds the threshold is the residual update and channel response vector 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 residual threshold in pattern detection based on the updated residual and detection pattern response value, thereby enhancing the detection probability of the main path and suppressing interference patterns, and improving estimation accuracy and robustness. The pattern detection response in the lazy residual update mechanism specifically includes the following steps: (1) Calculate the mode detection response value: based on the current residual matrix Joint angle-distance dictionary with polarization domain Based on the projection results, the detection response values ​​of all candidate angle-distance patterns are calculated using the following formula: ； in, To detect the response value; For the joint angle-distance dictionary of the polarization domain; The residual matrix; The weight vector matrix; The magnitude of the detection response value of the candidate angle-distance pattern represents the probability that the pattern corresponds to the actual channel path; the weight vector Used to enhance the current trusted mode direction; (2) Select the maximum response mode index: Select the joint mode index mode_idx with the largest detection response value pattern. This index corresponds to a specific angle-distance combination in the polarization domain two-dimensional dictionary and add it to the support set. ; The lazy residual update mechanism includes the following steps: The residual change threshold control update process includes the following steps: (3) Add the angle-distance combination corresponding to the position of the largest joint mode index selected in step (2) in the polarization domain two-dimensional dictionary to the support set. Then, calculate the difference between the current channel estimation residual and the channel estimation residual from the previous round. ,in, Denotes the Frobenius norm; Estimate the residual for the current channel; The residual from the previous round of channel estimation; If the difference between the current channel estimation residual and the previous channel estimation residual is less than a preset residual threshold If the channel estimation has not been effectively improved at this point, then skip the current round of residual update and channel estimation. If the difference between the current channel estimation residual and the previous channel estimation residual is greater than a preset residual threshold... If the channel estimation performance is significantly improved at this point, then the channel response vector and residual need to be updated. S3. Termination and Output: The enhanced algorithm, which uses a lazy residual update mechanism and an adaptive weight adjustment mechanism, outputs the estimated channel matrix in the polarization domain when the residual is below the threshold or exceeds the maximum iteration value.

2. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, In step S1, the channel model of the near-field region of the near-field system is established using orthogonal frequency division multiplexing (OFDM) technology. The base station is equipped with a large number of antenna arrays. The signal emitted by the user propagates in the near-field scenario, and the signal is characterized by a sparse structure in the polarization domain. The channel propagation characteristics in the near-field polarization domain conform to the spherical wavefront model. The channel response is constructed based on the spherical wavefront effect, and the channel response is specifically expressed as follows: ； in, The array response vector representing the path of the channel in the polarization domain. c is the speed of light.

3. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, In step S2, the initialization settings are first performed, as follows: Set the residual matrix ,in For the received signal matrix; initial support set For an empty set; construct the initial weight matrix. ; Set initial residual threshold And set a threshold decrease factor. ; Set the maximum number of iterations t; based on the above initialization parameters, construct the iterative support set using an iterative algorithm with a lazy residual update mechanism and an adaptive weight adjustment mechanism.

4. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, The channel response vector and residuals are updated specifically by using a least squares estimation algorithm for the modes within the support set, as shown in the following formula: ； ； in, To update the channel response vector; This is the updated channel residual; Indicates the sub-dictionary corresponding to the support set, superscript This indicates the transpose. This represents the received signal vector.

5. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, In step S2, the weight vector in the dynamic adjustment mode detection of the adaptive weight adjustment mechanism specifically includes the following steps: (4) Based on the updated residual distribution and detection response pattern value in step (3), dynamically update the weight vector according to factors such as the contribution of each pattern to the residual and energy intensity. To enhance the main path detection rate and suppress interfering patterns, the function formula used to update the weight vector is as follows: ； in, For the updated channel residual, To detect the response value, This is the updated weight vector.

6. 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, It also includes dynamically adjusting the residual update threshold, using a decreasing residual update threshold during iteration to promote algorithm convergence. The specific calculation formula is as follows: ,in .

7. The enhanced near-field polarization domain channel estimation method based on lazy residual update and adaptive weight adjustment mechanism according to claim 1, characterized in that, Step S3 includes a termination decision: if the channel residual in this round is lower than a set threshold. If 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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