Super-large scale MIMO mixed field channel estimation method based on dictionary learning

Abstract

The invention provides a super-large-scale MIMO mixed field channel estimation method based on dictionary learning, and the method comprises the steps: building a mixed field channel scene which comprises a base station, at least one near-field scatterer, at least one far-field scatterer and K users; constructing a mixed field channel model according to the mixed channel scene; and establishing a hybrid field channel recovery problem according to the hybrid field channel model and solving the hybrid field channel recovery problem. According to the method, the channel characteristics of the mixed field channel can be well captured, and accurate estimation is realized.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more specifically, to a very large-scale MIMO mixed-field channel estimation method based on dictionary learning. Background Art

[0002] In the existing far-field and near-field channel models, it is assumed that all scatterers are in the far-field or near-field region. However, in actual scenarios, scatterers usually appear partially in the far-field region and partially in the near-field region. Most of the existing ultra-large-scale MIMO mixed-field channel estimation methods use far-field dictionaries and near-field dictionaries for channel estimation for far-field channels and near-field channels, respectively, but these methods are not suitable for such mixed-field communication scenarios. In addition, in the mixed-field channel model, both the far-field dictionary and the near-field dictionary only use the characteristics of the far-field channel or the near-field channel, and cannot accurately represent the characteristics of the mixed-field channel. Summary of the invention

[0003] In order to overcome the defect that the above-mentioned existing mixed field channel estimation method cannot estimate channel characteristics well, the present invention provides a very large-scale MIMO mixed field channel estimation method based on dictionary learning.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] The present invention provides a method for estimating a very large-scale MIMO mixed-field channel based on dictionary learning, comprising the following steps:

[0006] Establishing a mixed-field channel scenario, the scenario comprising a base station, at least one near-field scatterer, at least one far-field scatterer, and K users;

[0007] Constructing a mixed field channel model according to the mixed channel scenario;

[0008] A mixed field channel restoration problem is established and solved according to the mixed field channel model.

[0009] Preferably, constructing a mixed field channel model according to the mixed channel scenario includes:

[0010] y=Ph hybrid +n

[0011] Where y represents the user's received signal, Where N RF Indicates the number of base station radio chains, N RF <<N, where N is the number of antennas equipped by the base station, S is the number of time periods, (·) T represents transpose, represents the overall observation matrix, p S represents the transmission pilot sequence of the user in the Sth time slot, represents the configuration simulation combination matrix of the Sth time slot base station, h hybrid represents the mixed field channel, represents noise, h hybrid The calculation formula is:

[0012]

[0013] Among them, L f and L n are the number of far-field and near-field path components, respectively, L = L f +L n is the number of all path components, and Respectively represent the first f The gains and angles of the far-field path components, d and λ c Represents antenna spacing and carrier wavelength, d = λ c / 2, From the actual physical perspective, Indicates the first f The steering vector of the far-field path component is:

[0014]

[0015] in,(·) H represents conjugate transpose;

[0016] Indicates the first n The gain of the path component, and Indicates the first n The angle and distance of the secondary near-field scattering from the center of the antenna array, Indicates the kth n The distance from the near-field scatterer to the nth antenna of the base station,

[0017] Preferably, establishing and solving a mixed field channel recovery problem according to the mixed field channel model includes:

[0018] Establishing the hybrid field channel recovery problem;

[0019] Convert the mixed field channel recovery problem into a dictionary learning problem P1;

[0020] Transform the dictionary learning problem P1 into the dictionary learning problem P2;

[0021] Transform the dictionary learning problem P2 into the dictionary learning problem P3;

[0022] Solve the dictionary learning problem P3.

[0023] Preferably, the establishing of the mixed field channel recovery problem comprises:

[0024] The mixed field channel h hybrid Convert to sparse representation:

[0025]

[0026] in, h hybrid The sparse representation of is the value to be estimated, and D is The corresponding hybrid dictionary;

[0027] The mixed-field channel sparse recovery problem is expressed as:

[0028]

[0029] in, Represents the L0 norm, which means finding the vector The number of non-zero entries in .

[0030] Preferably, the converting the mixed field channel recovery problem into a dictionary learning problem P1 comprises:

[0031] P1:

[0032] in,

[0033] Preferably, the converting the dictionary learning problem P1 into the dictionary learning problem P2 comprises:

[0034] according to The separable structural characteristics of in represents the Kronecker product, and then the dictionary learning problem P1 is transformed into:

[0035] P2:

[0036]

[0037] Where Y = [y1,…,y K ], y K represents the received signal of the Kth user, Denote the sparse representation of the mixed field channel of the Kth user.

[0038] Preferably, the converting the dictionary learning problem P2 into the dictionary learning problem P3 comprises:

[0039] P3:

[0040] in,

[0041] Preferably, solving the dictionary learning problem P3 includes:

[0042] Dictionary learning problem P3 about A, B and Are non-convex, optimize the dictionary separately And dictionaries A and B, the specific steps are as follows:

[0043] S51: Initialization dictionary k = 1, maximum number of iterations M, calculation;

[0044] S52: Calculate the dictionary for the kth iteration

[0045] Fixed dictionary A, dictionary B, dictionary learning problem P3 is transformed into about dictionary The optimization problem is:

[0046]

[0047] Obtaining sparse representation using orthogonal matching pursuit algorithm according to get where reshape(·) represents the matrix rearrangement function;

[0048] S53: Re-update Calculate the k-th iteration dictionary A (k) ;

[0049] Fixed sparse representation Dictionary B, dictionary learning problem P3 is transformed into an optimization problem about dictionary A:

[0050]

[0051] The gradient projection method is used to solve this problem. (k) for:

[0052]

[0053] Here, normalize means that all columns are matrices with unit Euclidean norm.

[0054] S54: A (k) Re-update A and calculate the k-th iteration dictionary B (k) ;

[0055] Fixed sparse representation Dictionary A, dictionary learning problem P3 is transformed into an optimization problem about dictionary B:

[0056]

[0057] The gradient projection method is used to solve this problem. (k) for:

[0058]

[0059] S55: Determine whether k ≥ M, if yes, execute step S57, otherwise execute step S56;

[0060] S56: B (k) Re-update B, execute k′=k+1, and re-update k′ to k, and return to step S52;

[0061] S57: Get A (M) , B (M) , according to A (M) , B (M) , using the orthogonal matching pursuit algorithm to obtain a sparse representation as the mixed field channel h hybrid .

[0062] Preferably, in step S52, obtaining a sparse representation by using an orthogonal matching pursuit algorithm includes:

[0063]

[0064] y temp =vectorize(Y)

[0065]

[0066] Where vectorize(·) represents the vectorization operation, OMP represents the orthogonal matching pursuit algorithm, and s represents the sparsity factor.

[0067] The present invention also provides a synaesthesia integrated system based on hybrid intelligent reflective surface assistance, which is used to implement the above method, including:

[0068] A scenario establishment module, used to establish a mixed field channel scenario, the scenario including a base station, at least one near-field scatterer, at least one far-field scatterer and K users;

[0069] A model building module, used to build a mixed field channel model according to the mixed channel scenario;

[0070] The problem solving module is used to establish and solve the mixed field channel restoration problem according to the mixed field channel model.

[0071] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0072] The present invention proposes a method for estimating a mixed-field channel of a very large-scale MIMO based on dictionary learning, establishes a mixed-field channel scenario, and the scenario includes a base station, at least one near-field scatterer, at least one far-field scatterer, and K users; constructs a mixed-field channel model according to the mixed-field channel scenario; and establishes and solves a mixed-field channel recovery problem according to the mixed-field channel model. The present invention can well capture the channel characteristics of the mixed-field channel and achieve accurate estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of the ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning described in Example 1;

[0074] Figure 2 A system block diagram of the ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning described in Example 2;

[0075] Figure 3 This is a comparison chart of channel estimation performance of different dictionaries under various signal-to-noise ratios described in Example 2;

[0076] Figure 4 This is a comparison chart of channel estimation performance of different dictionaries in Example 2 at various distances between users or scattering clusters;

[0077] Figure 5 This is a comparison chart of channel estimation performance of different dictionaries under different pilot lengths described in Example 2;

[0078] Figure 6 This is a schematic diagram of the structure of the ultra-large-scale MIMO mixed-field channel estimation system based on dictionary learning described in Example 3. DETAILED DESCRIPTION

[0079] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0080] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0081] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0082] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0083] Example 1

[0084] This embodiment provides a method for estimating a mixed-field channel of a very large-scale MIMO based on dictionary learning. Figure 1 As shown, the following steps are included:

[0085] S1: Establishing a mixed-field channel scenario, the scenario including a base station, at least one near-field scatterer, at least one far-field scatterer, and K users;

[0086] S2: constructing a mixed field channel model according to the mixed channel scenario;

[0087] S3: Establishing and solving a mixed-field channel recovery problem according to the mixed-field channel model.

[0088] In the specific implementation process, a mixed field channel scenario is first established, then a mixed field channel model is constructed according to the mixed channel scenario, and finally a mixed field channel recovery problem is established according to the channel model and the channel characteristics of the mixed field channel are obtained by solving it.

[0089] Example 2

[0090] The present invention provides a method for estimating a very large-scale MIMO mixed-field channel based on dictionary learning, comprising the following steps:

[0091] S1: Establishing a mixed-field channel scenario, the scenario including a base station, at least one near-field scatterer, at least one far-field scatterer, and K users;

[0092] like Figure 2 , which is a system block diagram of the ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning described in this embodiment.

[0093] S2: constructing a mixed field channel model according to the mixed channel scenario;

[0094] Consider the uplink mixed-field signal model and channel model of a very large-scale MIMO communication system. The base station is equipped with N antennas. Due to the different positions of scatterers in the mixed-field channel scenario, there are not only near-field path components but also far-field path components. The antenna spacing is where λ c is the carrier wavelength. The base station is equipped with N RF The mixed wave number of the RF chain forms a structure, N RF <<N, where N represents the number of antennas equipped by the base station, p=[p1,p1,…,p s ,…,p S ], represents the transmission pilot sequence of the user in S time periods, In the sth time period, the received signal of the Kth user can be expressed as:

[0095] y s =D s hp s +Ds n s

[0096] in, represents the configurable simulation combination matrix of the base station in the sth time period, Indicates the uplink channel from the user to the base station.

[0097] In actual communication scenarios, the near-field region and the far-field region are separated by the Rayleigh distance Z = 2R 2 / λ c Determined by, where R represents the antenna array aperture. When the distance between the scatterer and the base station exceeds the Rayleigh distance, the far-field path component is introduced; when the distance between the scatterer and the base station is less than the Rayleigh distance, the near-field path component is introduced. Due to the limited number of scatterers, the far-field channel is sparse in the angular domain and the near-field channel is sparse in the polar domain. Using the sparsity of the channel, the traditional method based on channel state information can obtain the two channels separately. The channel in the hybrid field communication system can be expressed as:

[0098] h=h f +h n

[0099] Far-field channel path component h f It can be expressed as:

[0100]

[0101] Among them, L f Represents the number of far-field channel path components. l and θ l denote the gain and angle of the lth path component respectively. Due to the consumption of the far-field plane wavefront, the lth path component a(θ l ) can be expressed as:

[0102]

[0103] in, d and λ c denotes the antenna spacing and carrier wavelength respectively. l ∈(0,π) is the actual physical angle. Then the non-sparse far-field channel h f Converted into sparse angular domain representation:

[0104]

[0105] in, Denotes the discrete Fourier transform matrix. D f Contains N orthogonal steering vectors uniformly sampled from the entire angular space, D f =[a(θ0),…,a(θ N-1)],in is the corresponding sparse angular domain channel.

[0106] Similarly, h n The near-field channel introduced when the distance between the scatterer and the base station is less than the Rayleigh distance can be expressed as:

[0107]

[0108] Among them, L n Represents the number of near-field channel path components. l represents the gain of the lth path component. Due to the assumption of a near-field spherical wavefront, the lth path component b(θ l ,r l ) can be expressed as:

[0109]

[0110] Among them, θ l and r l Represents the angle and distance of the lth near-field scattering from the center of the antenna array. and θ l represents the distance from the lth near-field scatterer to the nth antenna of the base station, Similar to the far-field channel, the non-sparse near-field channel h n It can be converted to a sparse representation in the polar domain:

[0111]

[0112] in, is a sparse near-field polar channel, D n is the near-field domain dictionary, expressed as:

[0113]

[0114] Where, the sampling angle θ n is The uniform sampling is obtained, s n =1,2,…,S N Indicates that the sampling angle is θ n The number of sampling distances at To ensure D n The coherence between any two columns in is low enough that for the distance The sampling is non-uniform sampling.

[0115] Construct a mixed field channel model according to the mixed channel scenario:

[0116] y=Phhybrid +n

[0117] Where y represents the user's received signal, Where N RF Indicates the number of base station radio chains, N RF <<, S represents the number of time periods, (·) T represents transpose, represents the overall observation matrix, p S represents the transmission pilot sequence of the user in the Sth time slot, represents the configuration simulation combination matrix of the base station in the Sth time slot, where N represents the number of antennas equipped by the base station, and h hybrid represents the mixed field channel, represents noise, h hybrid The calculation formula is:

[0118]

[0119] Among them, L f and L n are the number of far-field and near-field path components, respectively, L = L f +L n is the number of all path components, and Respectively represent the first f The gains and angles of the far-field path components, d and λ c Represents antenna spacing and carrier wavelength, d = λ c / 2, From the actual physical perspective, Indicates the first f The steering vector of the far-field path component is:

[0120]

[0121] in,(·) H represents conjugate transpose;

[0122] Indicates the first n The gain of the path component, and Indicates the first n The angle and distance of the secondary near-field scattering from the center of the antenna array, Indicates the first n The distance from the near-field scatterer to the nth antenna of the base station,

[0123] S3: Establishing and solving a mixed-field channel recovery problem according to the mixed-field channel model.

[0124] The mixed field channel recovery problem is established and solved including:

[0125] (1) Establishing the hybrid field channel recovery problem

[0126] The mixed field channel h hybrid Convert to sparse representation:

[0127]

[0128] in, h hybrid The sparse representation of is the value to be estimated, and D is The corresponding hybrid dictionary;

[0129] The mixed-field channel sparse recovery problem is expressed as:

[0130]

[0131] in, Represents the L0 norm, which means finding the vector The number of non-zero entries in .

[0132] (2) Convert the mixed field channel recovery problem into a dictionary learning problem P1

[0133] P1:

[0134] in,

[0135] (3) Transform the dictionary learning problem P1 into the dictionary learning problem P2

[0136] according to The separable structural characteristics of in represents the Kronecker product, and then the dictionary learning problem P1 is transformed into:

[0137] P2:

[0138]

[0139] Where Y = [y1,…,y K ], y K represents the received signal of the Kth user, Denote the sparse representation of the mixed field channel of the Kth user.

[0140] (4) Transform dictionary learning problem P2 into dictionary learning problem P3

[0141] P3:

[0142] in,

[0143] (5) Solve the dictionary learning problem P3.

[0144] Dictionary learning problem P3 about A, B and Are non-convex, optimize the dictionary separately And dictionaries A and B, the specific steps are as follows:

[0145] S51: Initialization dictionary k = 1, maximum number of iterations M, calculation;

[0146] S52: Calculate the dictionary for the kth iteration

[0147] Fixed dictionary A, dictionary B, dictionary learning problem P3 is transformed into about dictionary The optimization problem is:

[0148]

[0149] Obtaining sparse representation using orthogonal matching pursuit algorithm Specifically:

[0150]

[0151] y temp =vectorize(Y)

[0152]

[0153] Where vectorize(·) represents the vectorization operation, OMP represents the orthogonal matching pursuit algorithm, and s represents the sparsity factor.

[0154] according to get where reshape(·) represents the matrix rearrangement function;

[0155] S53: Re-update Calculate the k-th iteration dictionary A (k) ;

[0156] Fixed sparse representation Dictionary B, dictionary learning problem P3 is transformed into an optimization problem about dictionary A:

[0157]

[0158] The gradient projection method is used to solve this problem.(k) for:

[0159]

[0160] Here, normalize means that all columns are matrices with unit Euclidean norm.

[0161] S54: A (k) Re-update A and calculate the k-th iteration dictionary B (k) ;

[0162] Fixed sparse representation Dictionary A, dictionary learning problem P3 is transformed into an optimization problem about dictionary B:

[0163]

[0164] The gradient projection method is used to solve this problem. (k) for:

[0165]

[0166] S55: Determine whether k ≥ M, if yes, execute step S57, otherwise execute step S56;

[0167] S56: B (k) Re-update B, execute k′=k+1, and re-update k′ to k, and return to step S52;

[0168] S57: Get A (M) , B (M) , according to A (M) , B (M) , using the orthogonal matching pursuit algorithm to obtain a sparse representation as the mixed field channel h hybrid .

[0169] In order to demonstrate the performance of the method, this embodiment performs algorithm simulation and compares it with other algorithms. Figure 3 As shown in the figure, it is a comparison chart of channel estimation performance of different dictionaries under various signal-to-noise ratio conditions. It can be seen that as the signal-to-noise ratio increases, the smaller the MMSE of the three dictionary channel estimations, the better the channel estimation performance. Moreover, compared with the far-field OMP algorithm and the near-field OMP algorithm, the channel estimation performance of the method proposed in this paper is better. Figure 4 As shown in Figure 1, it is a comparison chart of the channel estimation performance of different dictionaries at various distances between users or scattering clusters. It can be seen that when the distance is 20m-120m, the channel estimation performance of the method proposed in this paper is better. Figure 5As shown in Figure 1, it is a comparison chart of channel estimation performance of different dictionaries under different pilot lengths. It can be seen that as the pilot length increases, the smaller the MMSE of the three dictionary channel estimates, the better the channel estimation performance. The channel estimation performance of the method proposed in this paper is better than that of the far-field OMP algorithm and the near-field OMP algorithm.

[0170] Example 3

[0171] This embodiment provides a synaesthesia integrated system based on the assistance of a hybrid intelligent reflective surface, such as Figure 6 As shown, including:

[0172] A scenario establishment module, used to establish a mixed field channel scenario, the scenario including a base station, at least one near-field scatterer, at least one far-field scatterer and K users;

[0173] A model building module, used to build a mixed field channel model according to the mixed channel scenario;

[0174] The problem solving module is used to establish and solve the mixed field channel restoration problem according to the mixed field channel model.

[0175] The same or similar reference numerals correspond to the same or similar components;

[0176] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0177] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for channel estimation of ultra-large-scale MIMO mixed field based on dictionary learning, characterized in that: The following steps are involved: Establishing a mixed-field channel scenario, the scenario comprising a base station, at least one near-field scatterer, at least one far-field scatterer, and K users; Constructing a mixed field channel model according to the mixed channel scenario; A mixed field channel restoration problem is established and solved according to the mixed field channel model.

2. The ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning according to claim 1, characterized in that: Constructing a mixed field channel model according to the mixed channel scenario includes: y=Ph hybrid +n Where y represents the user's received signal, Where N RF Indicates the number of base station radio chains, N RF <<N, where N is the number of antennas equipped by the base station, S is the number of time periods, (·) T represents transpose, represents the overall observation matrix, p S represents the transmission pilot sequence of the user in the Sth time slot, represents the configuration simulation combination matrix of the Sth time slot base station, h hybrid represents the mixed field channel, represents noise, h hybrid The calculation formula is: Among them, L f and L n are the number of far-field and near-field path components, respectively, L = L f +L n is the number of all path components, and Respectively represent the first f The gains and angles of the far-field path components, d and λ c Represents antenna spacing and carrier wavelength, d = λ c / 2, From the actual physical perspective, Indicates the first f The steering vector of the far-field path component is: in,(·) H represents conjugate transpose; Indicates the first n The gain of the path component, and Indicates the first n The angle and distance of the secondary near-field scattering from the center of the antenna array, Indicates the first n The distance from the near-field scatterer to the nth antenna of the base station, 3. The ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning according to claim 1, characterized in that: Establishing and solving the mixed field channel recovery problem according to the mixed field channel model includes: Establishing the hybrid field channel recovery problem; Convert the mixed field channel recovery problem into a dictionary learning problem P1; Transform the dictionary learning problem P1 into the dictionary learning problem P2; Transform the dictionary learning problem P2 into the dictionary learning problem P3; Solve the dictionary learning problem P3.

4. The ultra-large-scale MIMO mixed-field channel estimation method based on dictionary learning according to claim 3, characterized in that: The problem of establishing a mixed field channel recovery includes: The mixed field channel h hybrid Convert to sparse representation: in, h hybrid The sparse representation of is the value to be estimated, and D is The corresponding hybrid dictionary; The mixed-field channel sparse recovery problem is expressed as: in, Represents the L0 norm, which means finding the vector The number of non-zero entries in .

5. The method for ultra-large-scale MIMO mixed-field channel estimation based on dictionary learning according to claim 4, characterized in that: The conversion of the mixed field channel recovery problem into a dictionary learning problem P1 includes: in, 6. The method for ultra-large-scale MIMO mixed-field channel estimation based on dictionary learning according to claim 5, characterized in that: The conversion of the dictionary learning problem P1 into the dictionary learning problem P2 includes: according to The separable structural characteristics of in represents the Kronecker product, and then the dictionary learning problem P1 is transformed into: Where Y = [y1,…,y K ], y K represents the received signal of the Kth user, Denote the sparse representation of the mixed field channel of the Kth user.

7. The method for ultra-large-scale MIMO mixed-field channel estimation based on dictionary learning according to claim 6, characterized in that: The conversion of the dictionary learning problem P2 into the dictionary learning problem P3 includes: P3: in, 8. The method for ultra-large-scale MIMO mixed-field channel estimation based on dictionary learning according to claim 7, characterized in that: Solving the dictionary learning problem P3 includes: Dictionary learning problem P3 about A, B and Are non-convex, optimize the dictionary separately And dictionaries A and B, the specific steps are as follows: S51: Initialization dictionary k = 1, maximum number of iterations M, calculation; S52: Calculate the dictionary for the kth iteration Fixed dictionary A, dictionary B, dictionary learning problem P3 is transformed into about dictionary The optimization problem is: Obtaining sparse representation using orthogonal matching pursuit algorithm according to get where reshape(·) represents the matrix rearrangement function; S53: Re-update Calculate the k-th iteration dictionary A (k) ; Fixed sparse representation Dictionary B, dictionary learning problem P3 is transformed into an optimization problem about dictionary A: The gradient projection method is used to solve this problem. (k) for: Here, normalize means that all columns are matrices with unit Euclidean norm. S54: A (k) Re-update A and calculate the k-th iteration dictionary B (k) ; Fixed sparse representation Dictionary A, dictionary learning problem P3 is transformed into an optimization problem about dictionary B: The gradient projection method is used to solve this problem. (k) for: S55: Determine whether k ≥ M, if yes, execute step S57, otherwise execute step S56; S56: B (k) Re-update B, execute k′=k+1, and re-update k′ to k, and return to step S52; S57: Get A (M) , B (M) , according to A (M) , B (M) , using the orthogonal matching pursuit algorithm to obtain a sparse representation as the mixed field channel h hybrid .

9. The method for ultra-large-scale MIMO mixed-field channel estimation based on dictionary learning according to claim 8, characterized in that: In step S52, the step of obtaining a sparse representation by using an orthogonal matching pursuit algorithm includes: y temp =vectorize(Y) Where vectorize(·) represents the vectorization operation, OMP represents the orthogonal matching pursuit algorithm, and s represents the sparsity factor.

10. A synaesthesia integrated system based on hybrid intelligent reflective surface assistance, used to implement the method described in any one of claims 1 to 9, characterized in that: include: A scenario establishment module, used to establish a mixed field channel scenario, the scenario including a base station, at least one near-field scatterer, at least one far-field scatterer and K users; A model building module, used to build a mixed field channel model according to the mixed channel scenario; The problem solving module is used to establish and solve the mixed field channel restoration problem according to the mixed field channel model.

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