Effective XL-MIMO system near-field parameter estimation and positioning method

Abstract

The invention relates to an effective XL-MIMO system near-field parameter estimation and positioning method. Considering that a communication scene with a challenging LoS path being shielded and a system of a large-scale antenna array are deployed at a receiving end and a transmitting end, the method comprises the following steps: firstly, utilizing a structural relation between a codebook matrix and a near-field parameter to realize preliminary extraction of the parameter; and then, iteratively refining the obtained parameters by using a steepest descent thought, thereby realizing super-resolution estimation of the parameters such as angle, distance and path gain. The obtained parameters can be further applied to a sensing link, accurate estimation of the position information of the user equipment and the scattering points is completed by means of geometric constraints of the system and the obtained high-precision parameters, and a basis is provided for development of various sensing services. In addition, the method provided by the invention is superior to the existing competitive method in parameter estimation precision and positioning precision, and can still maintain excellent performance under the conditions of low sampling number and low pilot frequency overhead.

Description

Technical Field

[0001] The technology relates to the field of wireless communications, and in particular to near-field parameter estimation in an XL-MIMO system and positioning of key nodes of the system. Background Art

[0002] As a promising technology in the sixth generation (6G) mobile communications, the extremely large-scale MIMO (XL-MIMO) technology is expected to meet the growing traffic demand and overcome the severe path loss in high-frequency communications. Compared with the traditional MIMO system, the number of antennas in the XL-MIMO system has increased significantly. At the same time, the use of high-frequency bands such as millimeter waves and terahertz makes the differences in the electromagnetic characteristics of wireless signals in different areas impossible to ignore. The communication area can be roughly divided into two areas: the near field and the far field, with the Rayleigh distance as the boundary. For the XL-MIMO system, with the increase in the number of deployed antennas and the increase in the operating frequency, the Rayleigh distance can be as high as hundreds of meters, which leads to a significant expansion of the near field range.

[0003] Channel estimation algorithms based on the far-field plane wave assumption experience performance degradation in the near-field region. The spherical wave assumption is widely used in near-field communications because it describes information in both the angle dimension and the distance dimension, which is closer to the real channel model. Under the spherical wave assumption, the near-field channel is determined by parameters such as angle, distance, and path gain. Therefore, implementing the estimation of channel parameters can not only reconstruct the near-field channel, but also help to locate the key nodes of the system.

[0004] In the paper by Z. Lu, Y. Han, S. Jin, and M. Matthaiou (Near-Field Localization and Channel Reconstruction for ELAA Systems [J]. IEEE Trans. Wireless Commun., vol. 23, no. 7, pp. 6938-6953, July 2024), a damped Newton-based orthogonal matching pursuit method is proposed to estimate the parameters in the near-field channel, but this method is only applicable to systems equipped with single-antenna users, and its application scope is limited. In the paper by Y. Lu and L. Dai (Near-Field Channel Estimation in Mixed LoS / NLoSEnvironments for Extremely Large-Scale MIMO Systems [J]. IEEE Trans. Commun, vol. 71, no. 6, pp. 3694-3707, June 2023), the channel parameters can be solved using the polarization domain orthogonal matching pursuit (PSOMP) algorithm, but the parameter estimation accuracy of this method needs to be further improved. Summary of the invention

[0005] Purpose of the invention: To overcome the deficiencies of the prior art, the present invention proposes an effective near-field parameter estimation and positioning method for the XL-MIMO system, so as to achieve accurate estimation of near-field parameters and accurate positioning of user equipment and environmental scattering points.

[0006] Technical solution: An effective XL-MIMO system near-field parameter estimation and positioning method described in the present invention includes:

[0007] In the near-field communication scenario where the LoS path is blocked, a system with ultra-large-scale uniform linear arrays deployed on both the transmitting and receiving ends uses the spherical wave hypothesis to construct the near-field channel and receiving signal model;

[0008] Design a sampling codebook matrix corresponding to the angle and distance parameters, and use the orthogonal matching pursuit algorithm to solve the index information related to the near-field parameters based on the correlation between the received signal and the codebook matrix.

[0009] Analyze the relationship between the obtained index and the parameters to be estimated, and realize the preliminary extraction of angle parameters and distance parameters according to the structural characteristics of the codebook matrix and the Crocker inner product operation rule;

[0010] Design an optimization function to refine the obtained parameters for super-resolution, and iteratively update the angle and distance parameters using the idea of ​​steepest descent;

[0011] Use the system's geometric constraints and estimated angle and distance parameters to sense key system nodes, thereby accurately locating user devices and environmental scattering points;

[0012] Furthermore, in the near-field communication scenario where the LoS path is blocked, for a system with ultra-large-scale uniform linear arrays deployed on both the transmitting and receiving ends, the spherical wave hypothesis is used to construct the near-field channel and receiving signal model, including:

[0013] Consider a communication system operating in the millimeter wave frequency band. Due to the introduction of ultra-large-scale arrays and the use of high-frequency operating frequency bands, the Rayleigh distance can reach hundreds of meters, and the coverage of the near-field area is significantly expanded. User equipment and scattering points are all located in the near-field radiation area.

[0014] In the proposed system, both the base station and the user equipment are equipped with ultra-large-scale uniform linear arrays, with the number of antennas being and Although high-frequency signals are beneficial to improving the communication rate and spectrum efficiency of the system, the severe path loss and blocking sensitivity caused by high-frequency electromagnetic waves make the LoS path between the base station and the user equipment easily blocked by dense environments. This situation is common in reality and is very challenging.

[0015] In order to cope with such challenging communication scenarios and achieve effective near-field communication and perception, the present invention focuses on a near-field communication system in which the LoS path is blocked and the communication between the transceiver and the receiver is only achieved by the NLoS path.

[0016] Considering the electromagnetic wave characteristics of the near field, the present invention adopts a more accurate spherical wave model for channel modeling to better simulate the real communication channel. Compared with the traditional plane wave model, the spherical wave model adds a description of the distance dimension in addition to the angle dimension, providing a basis for simultaneous communication and perception. The corresponding channel expression is as follows:

[0017]

[0018] in, is the number of paths, It is The path gain corresponding to the path, . and Corresponding to the The departure angle and arrival angle of each path. and The center point of the transceiver array and the The distance between the scattering points. represents the conjugate transpose operation, and are the steering vectors of the transmitting and receiving ends respectively. The steering vector of the antenna under the spherical wave model is not only related to the angle parameter but also affected by the distance parameter. Its expression is as follows:

[0019]

[0020]

[0021] in, is the carrier frequency, is the speed of light, and and They are the first and second antenna arrays of the base station and user equipment, respectively. and Antenna and The distance between the scattering points is expressed as:

[0022]

[0023]

[0024] in, , .

[0025] To facilitate subsequent processing, the channel The matrix mode is given accordingly: .in, , , .

[0026] Assume that the base station sends pilot signal, then the signal received at the user equipment end can be written as:

[0027]

[0028] in, is satisfied The precoding matrix with constant modulus constraint, To satisfy The combination matrix of constant modulus constraints, is the corresponding received signal dimension, is a noise matrix that follows a Gaussian distribution.

[0029] Furthermore, a sampling codebook matrix corresponding to the angle and distance parameters is designed. According to the correlation between the received signal and the codebook matrix, the idea of ​​the orthogonal matching pursuit algorithm is used to solve and obtain the index information related to the near-field parameters, including:

[0030] By uniform sampling, the sampling values ​​of given angle and distance are obtained. is the sampling value of the departure angle, is the sampled value of the arrival angle, is the distance sample value between the center of the base station array and the scattering point and is the distance sampling value between the center of the user equipment array and the scattering point, where Corresponding to the angle and distance sampling numbers of the transmitting and receiving ends respectively. In order to facilitate the subsequent parameter extraction, it is defined here , , ,and .

[0031] Substituting the above parameter sampling information into the steering vector expression corresponding to the spherical wave model and combining them, the codebook matrix can be designed and , which is expressed as follows:

[0032]

[0033]

[0034] in, , .

[0035] With the help of the correlation of the matrix structure, the corresponding index information can be obtained through the idea of ​​greedy search. Specifically, firstly, the initial residual and the initial perception matrix ,vector It is a vector that stores the index obtained at each iteration, and the number of iterations is equal to the number of paths .

[0036] definition and , using the designed codebook, the residual is sparsely processed with the idea of ​​orthogonal matching pursuit algorithm, so as to obtain the sparse representation of the residual matrix in the angle domain and distance domain:

[0037]

[0038] Afterwards, Vectorize and find the index corresponding to the maximum value in the obtained vector, that is,

[0039]

[0040] in, , Representation Matrix No. Column, Symbol represents the Kronecker product, represents the conjugate operation, Represents a vectorized operation.

[0041] After completing the above steps, you can update accordingly From the analysis, we can see that the obtained index It carries information related to the target angle and distance parameters. Therefore, by analyzing the intrinsic structural association between the codebook matrix and the channel matrix, the perception matrix can be updated. The specific operations are as follows:

[0042]

[0043] in, , . represents the transpose operation, Indicates a round-up operation.

[0044] Using the solution The sparse channel vector is reconstructed to obtain:

[0045]

[0046] in, Represents the inverse operation, mapping the obtained vector into a matrix form, that is, . Next, update the residual as follows:

[0047]

[0048] Repeat the above process until the maximum number of iterations is reached. At this point, the index vector is obtained. and a rough estimate of the channel .

[0049] Furthermore, the correlation between the obtained index and the parameter to be estimated is analyzed, and the preliminary extraction of the angle parameter and the distance parameter is realized according to the structural characteristics of the codebook matrix and the Crocker inner product operation rule, which specifically includes:

[0050] remember , from the analysis, we can know that the vector Each index stored in points to The row in that is most correlated with the current residual. contains all the combinations of angle and distance sampling information and is calculated by the Kronecker product. This means Each row in corresponds to a set of sampled values ​​for a certain set of angle and distance parameters.

[0051] Therefore, using the obtained index information and the Kronecker product operation rule, the angle and distance parameters can be initially extracted to obtain , , , ,in,

[0052]

[0053]

[0054]

[0055]

[0056] After the initial extraction of the angle and distance parameters, the corresponding path gain can be solved by the least squares method. The solution process is as follows:

[0057]

[0058] in, , So far, the present invention has obtained the departure angle, arrival angle, distance between the center of gravity of the array at the base station end and the scattering point, distance between the center of gravity of the array at the user equipment end and the scattering point, and preliminary estimation of the channel gain, that is, These parameter information not only helps to reconstruct the near-field channel, but can also be applied to the perception link to assist the system in estimating the position of the user equipment and locating the scattering points.

[0059] Furthermore, an optimization function is designed to refine the obtained parameters for super-resolution, and the angle and distance parameters are iteratively updated using the idea of ​​steepest descent, including:

[0060] Using the idea of ​​maximum likelihood, the optimization function is designed as follows:

[0061]

[0062] in, represents the operation of taking the F norm, , is a given regularization parameter, is a diagonal matrix, For the modulo operation, is a constant that ensures that the logarithmic function makes sense.

[0063] Next, the objective function is iteratively optimized, given the number of iterations , and the Since the near-field model takes the distance dimension into consideration, the number of parameters that need to be updated in the optimization process increases significantly. In order to reduce the computational burden of the optimization process, the present invention explores the connection between the optimization of path parameters and the optimization of angle distance parameters.

[0064] Specifically, for the optimization function Ask about The partial derivative of can get the optimal solution of path parameters ,and It is a function of angle and distance, and its expression is as follows:

[0065]

[0066] in, , , , .

[0067] Will Substituting the expression into the optimization function, we can get:

[0068]

[0069] Next, the above preliminary extraction process As the initial value of the parameter refinement phase, the following parameter updates are performed:

[0070]

[0071]

[0072]

[0073]

[0074] in, is the update step size, , , and They are right , , and The partial derivative of corresponds to the gradient descent direction of the angle and distance parameters of the transmitting and receiving ends.

[0075] Specifically, the Take the path as an example, let , It can be calculated by the following formula:

[0076]

[0077] in, , .

[0078] The expression can be written as ,in, , , .

[0079] Similarly, the center of the base station array is The distance update direction between the scattering points of each path can be calculated by the following formula:

[0080]

[0081] in,

[0082]

[0083]

[0084]

[0085] because, The expression of similar, The expression of Similarly, it is omitted here for the sake of brevity.

[0086] Use the idea of ​​steepest descent to complete After the update, the corresponding , and the process is repeated until the iteration ends. Finally, the super-resolution estimation of the parameters is achieved, and the final result is recorded as Substituting the obtained parameters into the channel model, the final estimate of the near-field channel can be obtained. Thus, the present invention realizes high-precision estimation of near-field parameters such as angle, distance and path gain and accurate reconstruction of the near-field channel matrix.

[0087] Furthermore, the geometric constraints of the system and the estimated angle and distance parameters are used to sense the key nodes of the system, so as to accurately locate the user equipment and environmental scattering points, including:

[0088] By performing geometric analysis on the system and combining the obtained high-precision parameters with the geometric relationship between nodes, the scattering points in the environment can be located first. The position of the scattering point corresponding to each path can be calculated by the following formula, that is,

[0089]

[0090] After obtaining the scattering point positions corresponding to all paths, the obtained scattering point position information can be used as known information to assist in locating the user equipment. The formula for solving the position of the user equipment is given as follows:

[0091]

[0092] Thus, the present invention realizes accurate estimation of the positions of user equipment and scattering points in a near-field environment.

[0093] Beneficial effects: Compared with the prior art, the main advantages of the present invention are: for systems with extremely large-scale antenna arrays deployed at both the transmitting and receiving ends, an efficient near-field parameter estimation and perception method is proposed. By making full use of the structural characteristics of the codebook matrix and the antenna steering vector, the present invention provides an effective near-field parameter extraction method, and refines the parameters initially extracted using the idea of ​​steepest descent, thereby achieving high-precision estimation of parameters such as angle, distance, and path gain. In addition, the obtained high-precision parameters can be further applied to the perception link. With the help of the system's geometric constraints and the obtained parameter information, the present invention achieves accurate estimation of user equipment and scattering point location information, providing a basis for the development of diverse perception services. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 A flow chart of a method for realizing near-field parameter estimation and system key node positioning in the XL-MIMO system of the present invention;

[0095] Figure 2 It is a schematic diagram of the structure of the XL-MIMO system of the present invention;

[0096] Figure 3 is a performance diagram of the root mean square error (RMSE) of parameter estimation of the present invention and the existing PSOMP method under different signal-to-noise ratios (SNR);

[0097] Figure 4 This is a positioning RMSE performance diagram of the present invention and the existing PSOMP method under different SNRs;

[0098] Figure 5 The number of samples at different distances when SNR=30dB is Parameter estimation and positioning RMSE performance graph under;

[0099] Figure 6 The present invention is that when SNR=10dB, different pilot numbers Parameter estimation and positioning RMSE performance graph under; DETAILED DESCRIPTION

[0100] In order to make the features and advantages of the present invention more obvious and understandable and to define the protection scope of the present invention more clearly, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0101] Figure 2 FIG. 1 is a schematic diagram of the XL-MIMO system structure of the present invention, as shown in FIG. Figure 2 As shown in Figure 2, consider an XL-MIMO system with ultra-large-scale uniform linear arrays at both the transmitting and receiving ends, where the base station and user equipment are both located in the near field area and are respectively equipped with and In this system, the LoS path is blocked and the communication process is completed only by the NLoS path. The location of the base station is known, and the location of the user equipment and the scattering point needs to be estimated.

[0102] Implementation Example 1

[0103] See also Figure 3 and Figure 4 , Figure 3 and Figure 4 RMSE performance diagrams of parameter estimation and positioning of the present invention and the existing PSOMP method under different SNRs. The system parameters are set as: , , , , , , , . Establish a coordinate system with the base station location as the origin, and give the location of the user equipment to be estimated , and the coordinates of the scattering points , , , .from Figure 3 and Figure 4 It can be found that the RMSE performance of the proposed method in near-field parameter estimation and user equipment and scattering point positioning is always better than the existing PSOMP algorithm. With the improvement of system SNR, the overall performance of the proposed method is significantly improved, while the parameter estimation accuracy and positioning effect of the existing PSOMP algorithm are not ideal under low and medium signal-to-noise ratio conditions. When SNR=15dB, the parameter estimation accuracy of the proposed method can reach arrive The positioning performance is of the order of magnitude and can achieve centimeter-level positioning.

[0104] Implementation Example 2

[0105] See also Figure 5 , Figure 5The number of samples at different distances when SNR=30dB is Parameter estimation and positioning RMSE performance graph under ; except for the number of distance samples In addition, the other system parameter settings are consistent with Example 1. In Example 2, we set the change trend of the distance sampling number to: .Depend on Figure 5 It can be observed that when the number of samples of the codebook matrix increases to a certain value, that is, Figure 5 middle When , the parameter estimation accuracy of the proposed method reaches the lower bound and no longer decreases with This feature provides reference information for how to choose the appropriate number of samples. In addition, Figure 5 It also shows that the proposed method has good performance in near-field parameter estimation and positioning even when the sampling number is small.

[0106] Implementation Example 3

[0107] See also Figure 6 , Figure 6 The present invention is that when SNR=10dB, different pilot numbers Parameter estimation and positioning RMSE performance diagram under . The other system parameter settings are the same as those in Example 1. The pilot frequency number change trend in Example 3 is set to: .Depend on Figure 6 It can be observed that with With the increase of , the parameter estimation and positioning performance of the proposed method are improved accordingly. Figure 6 It can also be seen from the figure that the proposed method has an advantage in saving pilot overhead, even in Under the condition of , the proposed method can still provide accurate parameter estimation and centimeter-level positioning services.

[0108] In summary, the present invention considers an effective near-field parameter estimation and positioning method for XL-MIMO systems. Facing the more challenging communication scenario where the LoS path is blocked, the present invention first uses the spherical wave hypothesis to accurately construct the near-field channel, and then introduces a designed codebook matrix. By fully exploring the structural relationship between the codebook matrix and the parameters to be estimated, the channel parameters are initially extracted. Subsequently, the parameters are iteratively super-resolution optimized using the steepest descent method. Finally, by analyzing the geometric constraints of the system and combining them with the obtained high-precision parameters, accurate positioning of user equipment and scattering points is achieved. The proposed method is superior to existing competitive methods in both parameter estimation accuracy and positioning accuracy, and can still maintain superior performance under the conditions of low sampling number and low pilot overhead.

[0109] The above is only one of the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be thought of by a person skilled in the art within the technical scope disclosed by the present invention without creative work should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope defined in the claims.

Claims

1. An effective near-field parameter estimation and positioning method for XL-MIMO system, characterized in that The method includes: In the near-field communication scenario where the LoS path is blocked, a system with ultra-large-scale uniform linear arrays deployed on both the transmitting and receiving ends uses the spherical wave hypothesis to construct the near-field channel and receiving signal model; Design a sampling codebook matrix corresponding to the angle and distance parameters, and use the orthogonal matching pursuit algorithm to solve the index information related to the near-field parameters based on the correlation between the received signal and the codebook matrix. Analyze the relationship between the obtained index and the parameters to be estimated, and realize the preliminary extraction of angle parameters and distance parameters according to the structural characteristics of the codebook matrix and the Crocker inner product operation rule; Design an optimization function to refine the obtained parameters for super-resolution, and iteratively update the angle and distance parameters using the idea of ​​steepest descent; The system's geometric constraints and estimated angle and distance parameters are used to sense the key nodes of the system, thereby achieving accurate positioning of user devices and environmental scattering points.

2. An effective XL-MIMO system near-field parameter estimation and positioning method according to claim 1, characterized in that: In the near-field communication scenario where the LoS path is blocked, a system with ultra-large-scale uniform linear arrays deployed on both the transmitting and receiving ends is constructed using the spherical wave assumption. Specifically, a communication system operating in the millimeter wave frequency band is considered, where the user equipment and scattering points are both located in the near-field radiation area. In the proposed system, both the base station and the user equipment are equipped with ultra-large-scale uniform linear arrays, with the number of antennas being and , the LoS path between the base station and the user equipment is blocked, and the system relies only on the NLoS path to achieve communication. Considering the electromagnetic wave characteristics of the near field, the spherical wave model is used for channel modeling. The corresponding channel expression is written as ， in, is the number of paths, It is The path gain corresponding to the path, , and Corresponding to the The departure angle and arrival angle in each path, and The center point of the transceiver array and the The distance between the scattering points, represents the conjugate transpose operation, and are the steering vectors of the transmitting and receiving ends respectively, and their expressions are as follows ， ， in, is the carrier frequency, is the speed of light, and and They are the first and second antenna arrays of the base station and user equipment, respectively. and Antenna and The distance between the scattering points is expressed as ， ， in, , , in order to facilitate subsequent processing, the channel The matrix mode of is also given accordingly, namely ,in, , , , assuming the base station sends pilot signal, then the signal received at the user equipment end is written as ， in, is satisfied The precoding matrix with constant modulus constraint, To satisfy The combination matrix of constant modulus constraints, is the corresponding received signal dimension, is a noise matrix that follows a Gaussian distribution.

3. An effective XL-MIMO system near-field parameter estimation and positioning method according to claim 1, characterized in that: The sampling codebook matrix corresponding to the angle and distance parameters is designed. According to the correlation between the received signal and the codebook matrix, the index information related to the near-field parameters is obtained by using the idea of ​​the orthogonal matching pursuit algorithm. Specifically, the sampling values ​​of the angle and distance of the transmitting and receiving ends are obtained by uniform sampling, that is, , , and ,in, Corresponding to the angle and distance sampling numbers of the transmitting and receiving ends respectively, in order to facilitate the subsequent parameter extraction, it is defined here , , ,and , substitute the above parameter sampling information into the steering vector expression corresponding to the spherical wave model and combine them to design the codebook matrix and ,writing ， ， in, , , given the initial residual and the initial perception matrix ,vector It is a vector that stores the index obtained at each iteration, and the number of iterations is equal to the number of paths , with the help of the correlation of the matrix structure, the corresponding index information can be obtained through the idea of ​​greedy search, and the definition and , the residual is sparsely processed, and the sparse representation of the residual matrix in the angle domain and distance domain is obtained as follows ， right Vectorize and find the index corresponding to the maximum value in the obtained vector , and update accordingly ,because It carries information related to the target angle and distance parameters. Therefore, by analyzing the intrinsic structural association between the codebook matrix and the channel matrix, the perception matrix can be updated. The specific operations are as follows: ， in, , , represents the transpose operation, symbol represents the Kronecker product, Indicates the rounding up operation, using Reconstruct the sparse channel to get ， in, It represents the inverse operation, mapping the obtained vector into a matrix form, and then updating the residual as follows ， And repeat the above process until the maximum number of iterations is reached.

4. The effective XL-MIMO system near-field parameter estimation and positioning method according to claim 1, characterized in that: Analyze the relationship between the obtained index and the parameters to be estimated, and according to the structural characteristics of the codebook matrix and the Crocker inner product operation rule, realize the preliminary extraction of angle parameters and distance parameters, specifically including: , from the analysis, we can know that Each row in corresponds to a set of sampled values ​​of angle and distance parameters. Therefore, the obtained index information and the Kronecker product operation rule are used to achieve the preliminary extraction of angle and distance parameters, thereby obtaining , , , ,in, ， ， ， ， Accordingly, the path gain is solved by the least squares method, and the solution process is as follows: ， in, , ,So far, the proposed method has obtained the initial estimation of the departure angle, the arrival angle, the distance between the center of gravity of the array at the base station end and the scattering point, the distance between the center of gravity of the array at the user equipment end and the scattering point, and the path gain, that is, .

5. The effective XL-MIMO system near-field parameter estimation and positioning method according to claim 1, characterized in that: Design an optimization function, refine the obtained parameters for super-resolution, and iteratively update the angle and distance parameters using the idea of ​​steepest descent. Specifically, the optimization function is designed as follows: ， in, represents the operation of taking the F norm, , is a given regularization parameter, is a diagonal matrix, For the modulo operation, is a constant that ensures that the logarithmic function is meaningful. The optimization function is specifically Ask about The partial derivative of , we get the optimal solution of the path parameters , and Substitute the expression into the optimization function to get ， in, , , , , Next, the above preliminary extraction process obtained As the initial value of the parameter refinement phase, the following parameter updates are performed ， ， ， ， in, is the update step size, , , and They are right , , and The partial derivative corresponds to the gradient descent direction of the angle and distance parameters of the transmitting and receiving ends, and the steepest descent idea is used to complete the After the update, the corresponding , repeat this process until the iteration ends, and record the final result as , and substitute the obtained parameters into the channel model to obtain the final estimate of the near-field channel .

6. An effective XL-MIMO system near-field parameter estimation and positioning method according to claim 1, characterized in that: The geometric constraints of the system and the estimated angle and distance parameters are used to perceive the key nodes of the system, so as to accurately locate the user equipment and the scattering points in the environment. Specifically, the system is geometrically analyzed, and the obtained high-precision parameters are combined with the geometric relationship between the nodes to locate the scattering points in the environment. Specifically, The scattering point position corresponding to each path will be calculated by the following formula, that is, ， After obtaining the scattering point positions corresponding to all paths, the obtained scattering point position information is regarded as known information to assist in locating the user equipment. The formula for solving the position of the user equipment is given as follows: ， Thus, accurate estimation of the positions of user equipment and scattering points in the near-field environment is achieved.

Images