Two-stage cascade channel estimation method, device, equipment, medium and product

Abstract

The invention relates to the technical field of wireless communication, and particularly provides a two-stage cascade channel estimation method and device, equipment, a medium and a product, and the method comprises the steps: carrying out the compressed sensing of a constructed initial system model, and obtaining a communication system model; calculating a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the communication system model; determining an index interval to which the angle of arrival belongs based on the grid index value; updating the sparse measurement matrix based on the index interval to obtain a sparse representation matrix; and obtaining a compressed cascade channel matrix based on the sparse representation matrix and the cascade channel matrix in the communication system model. The real angle of arrival can be obtained by determining the index interval to which the angle of arrival belongs, and the sparse measurement matrix is converted into the sparse representation matrix with lower dimensionality based on the sparsity of the angular domain, so that off-network errors are avoided, the calculation complexity is reduced, and the calculation precision and accuracy of the compressed cascade channel matrix are improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a two-stage cascade channel estimation method, device, equipment, medium and product. Background Art

[0002] In recent years, reconfigurable smart surfaces (RIS) have been proposed for use in wireless communication systems with their advantages of low hardware cost and low energy consumption to enhance the coverage and capacity of wireless communication systems. RIS is also recognized as one of the key technologies for the future sixth-generation wireless communication network (6G). In order to quantify channel characteristics (such as attenuation and phase) and compensate for the transmitted signal to reduce the difference between the transmitted signal and the received signal, channel estimation is required. Since the RIS-assisted massive multiple-input multiple-output (MIMO) antenna system consists of hundreds of antenna elements and includes two-stage cascade channels from user equipment (UE) to RIS and from RIS to base station (BS), the difficulty and complexity of channel estimation increases dramatically.

[0003] The existing two-stage cascade channel estimation method proposes that the cascade channels share completely common non-zero rows and partially common non-zero columns based on the different angles of different user devices to achieve channel estimation. However, this method does not take into account the problem of direction mismatch, has off-network errors, and violates the sparsity assumption, which not only leads to a sharp increase in pilot consumption and computational complexity, but also seriously affects the accuracy of cascade channel estimation. Summary of the invention

[0004] In view of the above problems, the present application is proposed, and the present application provides a two-stage cascade channel estimation method, device, equipment, medium and product.

[0005] According to one aspect of the present application, a two-stage cascade channel estimation method is provided, comprising:

[0006] Performing compressed sensing on the constructed initial system model to obtain a communication system model, wherein the initial system model includes a two-stage cascade channel from a user to a reconfigurable smart surface side channel and from a reconfigurable smart surface side to a base station side channel;

[0007] Calculating a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the initial system model;

[0008] Based on the grid index value, determine the index interval to which the angle of arrival belongs, where the angle of arrival is the angle of arrival of the signal received by the base station side;

[0009] Based on the index interval, the sparse measurement matrix is ​​updated to obtain a sparse representation matrix;

[0010] A compressed concatenated channel matrix is ​​obtained based on the sparse representation matrix and the concatenated channel matrix in the communication system model.

[0011] In addition, the two-stage cascade channel estimation method according to one aspect of the present application further includes:

[0012] Compressed sensing is performed on the constructed initial system model to obtain the communication system model, including:

[0013] Based on a first dictionary matrix and a second dictionary matrix of discrete grid points generated by the initial system model, updating the cascaded channels in the initial system model, the first dictionary matrix being a uniform discrete array of arrival angles on the base station side, and the second dictionary matrix being a uniform discrete array of arrival angles on the reconfigurable smart surface side;

[0014] Based on the updated cascade channel, the initial system model is rewritten to obtain the communication system model.

[0015] In addition, the two-stage cascade channel estimation method according to one aspect of the present application further includes:

[0016] Determining, based on the grid index value, an index interval to which the arrival angle belongs, includes:

[0017] Based on the grid index value, obtaining a right adjacent index value and a left adjacent index value of the grid index value;

[0018] Calculating a first channel coefficient corresponding to the left adjacent index value and a second channel coefficient corresponding to the right adjacent index value;

[0019] When the first channel coefficient is greater than or equal to the second channel coefficient, determining that the index interval to which the arrival angle belongs is an interval consisting of the left adjacent index value and the grid index value;

[0020] When the first channel coefficient is less than the second channel coefficient, it is determined that the index interval to which the arrival angle belongs is an interval consisting of the grid index value and the right adjacent index value.

[0021] In addition, the two-stage cascade channel estimation method according to one aspect of the present application further includes:

[0022] Based on the index interval, a sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix, including:

[0023] Selecting sample parameters in the index interval, and obtaining a support set of the angle of arrival at the base station side based on the sample parameters;

[0024] Calculating an array vector based on the support set and a first dictionary matrix of discrete grid points generated by the initial system model;

[0025] Based on the array vector, a sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix.

[0026] In addition, the two-stage cascade channel estimation method according to one aspect of the present application further includes:

[0027] Based on the sparse representation matrix and the cascaded channel matrix in the communication system model, a compressed cascaded channel matrix is ​​obtained, comprising:

[0028] Based on the sparse representation matrix, reducing the dimension of the cascade channel matrix in the communication system model to obtain a pattern formula including the compressed cascade channel matrix;

[0029] The compressed cascade channel matrix is ​​calculated based on an expectation-maximization algorithm and the mode formula.

[0030] In addition, the two-stage cascade channel estimation method according to one aspect of the present application further includes: calculating the compressed cascade channel matrix based on the expectation maximization algorithm and the mode formula, including:

[0031] Modeling the compressed cascade channel matrix based on an expectation-maximization algorithm and the pattern formula to obtain a modeling formula for the compressed cascade channel matrix;

[0032] Based on the modeling formula, a calculation formula for a first a posteriori parameter is obtained, where the first a posteriori parameter is a mean value of a random variable of the compressed cascade channel matrix;

[0033] Deforming the calculation formula of the first a posteriori parameter so that the second a posteriori parameter in the calculation formula is moved to the other side of the equal sign; and, according to the diagonal estimation criterion, obtaining the calculation formula of the diagonal vector of the second a posteriori parameter, deforming the calculation formula of the diagonal vector so that the second a posteriori parameter is moved to the other side of the equal sign in the calculation formula of the diagonal vector, wherein the second a posteriori parameter is the covariance of the random variable of the compressed cascade channel matrix;

[0034] Calculate the first a posteriori parameter based on an expectation-maximization algorithm, a calculation formula for the first a posteriori parameter after deformation, and a calculation formula for the diagonal vector after deformation;

[0035] The compressed cascade channel matrix is ​​calculated based on the first a posteriori parameter and the mode formula.

[0036] According to another aspect of the present application, a two-stage cascade channel estimation device is provided, comprising:

[0037] A compressed sensing module is used to perform compressed sensing on the constructed initial system model to obtain a communication system model, wherein the initial system model includes a two-stage cascade channel from a user to a reconfigurable smart surface side channel and from a reconfigurable smart surface side channel to a base station side channel;

[0038] A first calculation module, configured to calculate a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the initial system model;

[0039] An interval determination module, configured to determine, based on the grid index value, an index interval to which an angle of arrival belongs, wherein the angle of arrival is an angle of arrival of a signal received by the base station side;

[0040] An updating module, used for updating the sparse measurement matrix based on the index interval to obtain a sparse representation matrix;

[0041] The second calculation module is used to obtain a compressed cascade channel matrix based on the sparse representation matrix and the cascade channel matrix in the communication system model.

[0042] According to another aspect of the present application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method of the above aspect.

[0043] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method of the above aspect is implemented.

[0044] According to another aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method of the above aspect is implemented.

[0045] As will be described in detail below, according to a two-stage cascade channel estimation method, apparatus, equipment, medium and product of an embodiment of the present application, the actual arrival angle can be obtained by determining the index interval to which the arrival angle belongs, and the non-convex problem is converted into a convex optimization problem. Based on the sparsity of the angular domain, the sparse measurement matrix is ​​converted into a sparse representation matrix. The sparse representation matrix only includes a dictionary of the actual arrival angle, and has a lower dimension. It not only avoids off-grid errors, but also reduces the computational complexity and improves the computational precision and accuracy of the compressed cascade channel matrix.

[0046] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0048] Figure 1 is a flowchart illustrating the application of a two-stage cascade channel estimation method according to an embodiment of the present application.

[0049] Figure 2 is a flowchart illustrating the application of calculating a compressed cascade channel matrix according to an embodiment of the present application.

[0050] Figure 3 is another flowchart illustrating the application of the two-stage cascade channel estimation method according to an embodiment of the present application.

[0051] Figure 4 is a schematic diagram illustrating the structure of a two-stage cascade channel estimation device according to an embodiment of the present application.

[0052] Figure 5 It is a schematic diagram illustrating the structure of a computer device according to an embodiment of the present application.

[0053] Figure 6 is a schematic diagram illustrating a computer program product according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more obvious, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0055] In recent years, reconfigurable smart surfaces (RIS) have been proposed for use in wireless communication systems with their advantages of low hardware cost and low energy consumption to enhance the coverage and capacity of wireless communication systems. RIS is also recognized as one of the key technologies for the future sixth-generation wireless communication network (6G). In order to quantify channel characteristics (such as attenuation and phase) and compensate for the transmitted signal to reduce the difference between the transmitted signal and the received signal, channel estimation is required. Since the RIS-assisted massive multiple-input multiple-output (MIMO) antenna system consists of hundreds of antenna elements and includes two-stage cascade channels from user equipment (UE) to RIS and from RIS to base station (BS), the difficulty and complexity of channel estimation increases dramatically.

[0056] The existing two-stage cascade channel estimation method proposes that the cascade channels share completely common non-zero rows and partially common non-zero columns based on the different angles of different user devices to achieve channel estimation. However, this method does not take into account the problem of direction mismatch, has off-network errors, and violates the sparsity assumption, which not only leads to a sharp increase in pilot consumption and computational complexity, but also seriously affects the accuracy of cascade channel estimation.

[0057] In the above, a two-stage cascade channel estimation method, apparatus, device, medium and product according to an embodiment of the present application are described with reference to the accompanying drawings. By determining the index interval to which the arrival angle belongs, the true arrival angle can be obtained, and the non-convex problem is converted into a convex optimization problem. Based on the sparsity of the angular domain, the sparse measurement matrix is ​​converted into a sparse representation matrix. The sparse representation matrix only includes a dictionary of true arrival angles, and has a lower dimension. It not only avoids off-grid errors, but also reduces the computational complexity and improves the computational precision and accuracy of the compressed cascade channel matrix.

[0058] By deforming the calculation formula of the first a posteriori parameter and the diagonal vector so that the second a posteriori parameter in the calculation formula is moved to the other side of the equal sign, the inverse operation of the second a posteriori parameter itself is converted into a method for solving the equation, which greatly reduces the calculation complexity, is conducive to solving the underlying block sparsity problem, introduces mode coupling prior, and reduces pilot overhead.

[0059] To facilitate understanding of this embodiment, a two-stage cascade channel estimation method disclosed in the embodiment of the present application is first introduced in detail. The execution subject of the two-stage cascade channel estimation method provided in the embodiment of the present application is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal DiTital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the two-stage cascade channel estimation method can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0060] like Figure 1 As shown, it is a flowchart of a two-stage cascade channel estimation method provided in an embodiment of the present application, and the method includes S1-S5:

[0061] S1: Perform compressed sensing on the constructed initial system model to obtain the communication system model.

[0062] The communication system model includes a two-stage cascade channel from the user to the reconfigurable smart surface side channel and from the reconfigurable smart surface side to the base station side channel, which is applied to a large-scale multi-input multi-output communication system assisted by a reconfigurable smart surface (RIS). S1 specifically includes the following steps:

[0063] S1.1: Based on the first dictionary matrix and the second dictionary matrix of discrete grid points generated by the initial system model, the cascade channels in the initial system model are updated.

[0064] The first dictionary matrix is ​​a uniform discrete array of arrival angles at the base station side, and the second dictionary matrix is ​​a uniform discrete array of arrival angles at the reconfigurable smart surface side.

[0065] S1.2: Based on the updated cascade channel, the initial system model is rewritten to obtain a communication system model.

[0066] S2: Calculate the grid index value based on the base station measurement matrix and the sparse measurement matrix included in the communication system model.

[0067] S3: Determine the index interval to which the arrival angle belongs based on the grid index value.

[0068] The arrival angle is the arrival angle of the signal received by the base station side, and S3 specifically includes the following steps:

[0069] S3.1: Based on the grid index value, obtain the right adjacent index value and the left adjacent index value of the grid index value.

[0070] S3.2: Calculate a first channel coefficient corresponding to a left adjacent index value and a second channel coefficient corresponding to a right adjacent index value;

[0071] S3.3: When the first channel coefficient is greater than or equal to the second channel coefficient, determine that the index interval to which the arrival angle belongs is an interval consisting of a left adjacent index value and a grid index value;

[0072] When the first channel coefficient is less than the second channel coefficient, it is determined that the index interval to which the arrival angle belongs is an interval consisting of the grid index value and the right adjacent index value.

[0073] S4: Based on the index interval, the sparse measurement matrix is ​​updated to obtain a sparse representation matrix. S4 specifically includes the following steps:

[0074] S4.1: Select sample parameters in the index interval, and obtain a support set of the arrival angle at the base station side based on the sample parameters;

[0075] S4.2: Calculate the array vector based on the support set and the first dictionary matrix of discrete grid points generated by the initial system model;

[0076] S4.3: Based on the array vector, the sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix.

[0077] S5: Obtain a compressed cascade channel matrix based on the sparsely represented matrix and the cascade channel matrix in the communication system model.

[0078] S5 specifically includes the following steps:

[0079] S5.1: Based on the sparse representation matrix, the cascade channel matrix in the communication system model is reduced in dimension to obtain a pattern formula including a compressed cascade channel matrix;

[0080] S5.2: Based on the expectation maximization algorithm (EM algorithm) and the mode formula, the compressed cascade channel matrix is ​​calculated. Further, Figure 2 As shown, S5.2 includes the following steps:

[0081] S5.2.1: Modeling the compressed cascade channel matrix based on an expectation-maximization algorithm and a pattern formula to obtain a modeling formula for the compressed cascade channel matrix;

[0082] S5.2.2: Based on the modeling formula, obtain a calculation formula for a first a posteriori parameter, wherein the first a posteriori parameter is the mean of the random variables of the compressed cascade channel matrix;

[0083] S5.2.3: deforming the calculation formula of the first a posteriori parameter so that the second a posteriori parameter in the calculation formula is moved to the other side of the equal sign; and, according to the diagonal estimation criterion, obtaining the calculation formula of the diagonal vector of the second a posteriori parameter, deforming the calculation formula of the diagonal vector so that the second a posteriori parameter is moved to the other side of the equal sign in the calculation formula of the diagonal vector. Wherein, the second a posteriori parameter is the covariance of the random variable of the compressed cascade channel matrix;

[0084] S5.2.4: Calculate the first a posteriori parameter based on the expectation-maximization algorithm, the calculation formula of the first a posteriori parameter after deformation, and the calculation formula of the diagonal vector after deformation;

[0085] S5.2.5: Based on the first a posteriori parameter and the mode formula, a compressed cascade channel matrix is ​​calculated.

[0086] like Figure 3 As shown, it is another flow chart of the two-stage cascade channel estimation method provided in the embodiment of the present application. It should be noted that the symbols involved in this embodiment are explained as follows: (·) T represents transpose, (·) H represents the conjugate transpose, (·) -1 Indicates the inverse, represents pseudo-inverse; represents the Kronecker product; vec(·) represents the vectorization of the matrix; ‖Δ‖1and‖Δ‖2 represent the l1 norm and l2 norm of the matrix Δ; Tr(·) represents the trace of the matrix; represents a complex Gaussian distribution with mean vector μ and covariance matrix Σ; the operator diag(a) forms a diagonal matrix with its vector elements; Indicates the expected symbol. The method comprises steps 1-3:

[0087] Step 1: Establish a communication system model for RIS-assisted massive MIMO.

[0088] Consider an uplink RIS-assisted massive MIMO communication system consisting of a base station (BS) with M (M ≥ 1) antennas, a RIS with N (N ≥ 1) element uniform planar arrays, and a single-antenna user (UE). Without loss of generality, it is assumed that there is no direct link between the user and the BS, and only the reflection channel (BS-RIS channel) is considered. and RIS-UE channel ), so that the effective uplink cascade channel from the user to the base station side is defined as:

[0089]

[0090] Among them, G is the cascade channel, G RB is the BS-RIS channel, h UR It is the RIS-UE channel.

[0091] During channel estimation, different users (UEs) send orthogonal pilot sequences m to the BS in T time slots. t , For uplink cascade channel estimation, without loss of generality, assume that m t =1, the initial system model can be expressed as:

[0092] Y=GΓ+E(2)

[0093] in, Y is the base station measurement matrix received at the BS from the user UE, Γ represents the phase reflection matrix, E represents the additive white Gaussian noise (AWGN) matrix, satisfy Where σ represents the noise accuracy. According to the existing commonly used channel model, G RB and h UR Can be expressed as:

[0094]

[0095]

[0096] Among them, L c is the number of scatterer paths of RIS-BS, L g is the number of scatterer paths of UE-RIS. is the complex gain of the l1th path, is the angle of arrival (AoA) at the BS, is the angle of departure (AoD) at RIS, is the complex gain of the l2th path, is the angle of arrival (AoA) at the RIS, is the array steering vector associated with RIS, is the array steering vector associated with the BS. As a normalized function of x and p, the array steering vector is defined as:

[0097]

[0098] Where χ represents the number of elements, θ represents the function of AOA or AOD, and d represents the antenna spacing, which usually satisfies λ is the carrier wavelength.

[0099] Since in practice AoAs / AoDs are random continuous values ​​resulting in an infinite number of atoms, the continuous parameter space and Gridding,reduced to a finite number of grid points.,In order to reduce the consistency of redundant dictionaries, uniform sampling of the,virtual angle domain is adopted.,The AoA / AoD domains at the BS and RIS sides contain a set of finite,uniform discrete grid points, which are defined as:

[0100]

[0101] Among them, Θ (Bs) The AoA / AoD domain at the BS contains a set of finite uniform discrete grid points, Θ (RIS) The AoA / AoD domain of the RIS side contains a set of finite uniform discrete grid points, is the mth grid value corresponding to the AoA / AoD domain on the BS side, sin(φ (n) ) is the nth grid value corresponding to the AoA / AoD domain on the RIS side, M G and N G are the grid resolutions on the BS side and the RIS side, respectively.

[0102] Therefore, the array response matrix of the uniformly discretized grid points at the BS end (i.e., the first dictionary matrix) is The array response matrix (second dictionary matrix) of uniformly discrete grid points at the RIS end is Using the array response matrix and The cascaded channel G can be re-expressed in the virtual angle domain as:

[0103]

[0104] in, is a sparse representation of the concatenated channels, is the array response matrix of the uniformly discretized grid points at the RIS end. Further, the cascade channel G can be expressed as:

[0105]

[0106] In this embodiment, the following three characteristics are used to implement the two-stage cascade channel estimation method:

[0107] Characteristics 1. Dual structure characteristics.

[0108] From formula (9), we can see that there is only one non-zero element in the array response. Direction and direction, so The row and column indices depend on and

[0109]

[0110] Feature 2: Block structure feature.

[0111] Since the scatterers on the UE side are densely located and the scatterers are close to each other, this causes the AoDs of the UEs to be similar or adjacent, resulting in sparse support in the block.

[0112] Property 3: The BS-RIS perspective is quasi-static compared to the RIS-UE perspective.

[0113] Because the positions of the BS and RIS are fixed and usually placed at high places, while the position of the mobile user UE is variable, the arrival angle AoAs at the BS end can be regarded as static information, which means that it can be effectively estimated.

[0114] In order to make full use of the significant sparsity of the cascade channel G, this embodiment reformulates the channel estimation problem as a problem of recovering sparse signals. Substituting formula (8) into formula (2), the initial system model is rewritten as a communication system model:

[0115]

[0116] in, is the array response matrix of uniformly discrete grid points at the BS end, is a sparse representation of the concatenated channels, is the array response matrix of the uniformly discrete grid points at the RIS end, Γ is the phase reflection matrix, and E represents additive white Gaussian noise. With the help of sparse virtual representation and vector identity properties, Among them, A, B, and C represent matrices respectively, and the communication system model is further rewritten as:

[0117] y=Φh+e(11)

[0118] in, Φ represents the sparse measurement matrix, Based on characteristics 1 and 2, Φ and h can be expressed as:

[0119]

[0120] in, yes The mth row of ; is the mth submatrix of Φ. In order to avoid confusion, h m and Φ m can be regarded as the mth group of h and Φ respectively.

[0121] Step 2: Construct a sparse representation matrix.

[0122] Since the true AoAs grid value is located around the coarsely divided grid and depends on the nearest grid point in the dictionary matrix, the AoAs can be roughly estimated using the orthogonal projection strategy. The update h non-zero group support set is set to:

[0123]

[0124] in, yes The base number, finally Contains all non-zero group indices with respect to h. is the maximum correlation index. In the nth iteration, the index with the largest correlation is most likely to be the non-zero group of h. The calculation formula is:

[0125]

[0126] in is the residual matrix obtained from the previous iteration, is the set of support columns The submatrix selected from Φ in . Channel coefficients is obtained by solving the least squares problem. The BS side uniform discrete set Θ (BS)The most relevant index relative to the true AoAs is given by equation (15), which provides a coarse grid point estimate for each AoAs, i.e. However, considering the inherent grid mismatch problem of the grid method, in particular, this embodiment adopts a simple numerical optimization algorithm to re-estimate the AoAs on the BS side through random search to alleviate the energy leakage problem.

[0127] First, we use a rough estimate Limit the search space to and in, is the grid index value, and They are the grid index values The left adjacent index value and the right adjacent index value of . Calculate using the least squares method (Right now )'s first channel coefficient and (Right now )'s second channel coefficient The purpose is to further narrow the range of real physical angles. This means that the index range of the real AOA is Otherwise For simplicity, this embodiment represents the index interval of the AOA of each scatterer path as

[0128] Secondly, in the index interval Randomly select sample parameters Θ Δ , therefore, the preliminary support set of the re-estimated BS-side common AoAs is Defined as:

[0129]

[0130] This means that the dictionary is uniformly sampled The columns contain only the re-estimated support set The array response vector of in, is the AoAs re-estimated support set The size of . Therefore, the sparse representation matrix Re-expressed as:

[0131]

[0132] According to the above description, the global optimal value of the local optimization problem (ie, Formula 18) can be solved for the BS-side AoA of each scattering path.

[0133]

[0134] in, is the residual matrix that needs to be optimized in order to accurately estimate the next BS-side AoA value. and vertical connection, as the initial estimated channel coefficients, that is,

[0135] In search of optimal sample parameters In the process, the channel coefficient is defined to be static. Once the optimal sample parameters are found The maximum number of iterations J, refer to formula (16), the updated re-estimated support set is:

[0136]

[0137] Then, the corresponding channel coefficients Updated to:

[0138]

[0139] When the maximum number of iterations Z is met, It will gradually approach the true BS-side AoA. The re-estimated BS-side AoA is added at each iteration to gradually fill the sparse representation matrix

[0140] Since the scatterers on the BS side are sparse, the sparse representation dictionary achieves a significant dimensionality reduction in the column dimension, thereby achieving dimensionality reduction of the cascade channel. Therefore, formula (11) can be rewritten as:

[0141]

[0142] in, is the compressed cascade channel matrix.

[0143] In the case of the reduction of the cascade channel matrix h, the proposed algorithm can effectively suppress the impact of the off-grid error and help reduce the pilot overhead and computational complexity of the second-stage block-structured SBL algorithm.

[0144] Step 3: Use the EM algorithm to solve formula (21) and calculate the compressed cascade channel matrix

[0145] On the one hand, in view of the block structure sparsity of the channel, this embodiment introduces a mode coupling prior model. On the other hand, in view of the high computational complexity caused by matrix inversion in the traditional SBL algorithm, this embodiment introduces a covariance-free method. The details are as follows:

[0146] In order to obtain the characteristics of the transmitted symbol and its neighboring elements, a hyperparameter γ = {γ t-1 ,γ t ,γ t+1}, and Modeled as:

[0147]

[0148] in, γ t yes To facilitate the inference of γ, the elements of γ are usually modeled as independent gamma distributions, that is:

[0149]

[0150] Among them, the parameters {a, b} take very small positive numbers. 0≤β≤1, β is the coefficient and the pattern correlation parameter between adjacent coefficients. t →+∞, the corresponding emission signal and its neighboring elements Obviously, when β>0, the sparsity of each coefficient is controlled not only by its own hyperparameters but also by its neighboring hyperparameters. If β=0, formula (22) degenerates into the traditional SBL model, and the likelihood function of the received signal is:

[0151]

[0152] Where 1 / β = σ -2 represents the noise accuracy, satisfying the p(β)=Gamma(β|c,d) hyper-prior controlled by the parameters {c,d}. According to Bayes' theorem, combined with formulas (22) and (24), The posterior parameter distribution of is given by:

[0153]

[0154] Among them, the first a posteriori parameter μ is the compressed cascade channel matrix The mean of the random variables, the second a posteriori parameter Σ is the compressed cascade channel matrix The covariance of the random variables .

[0155] Based on the above hierarchical model, the E-step of the EM algorithm uses formulas (26) and (27) to calculate μ and Σ respectively to update However, due to the high dimension of the cascade channel G, the inverse calculation process of each iteration is very complicated and cumbersome when updating Σ. In order to reduce the computational complexity, this embodiment replaces the matrix inversion with the solution of the linear system. The mode-coupled hierarchical Gaussian prior is introduced to capture the block structure sparsity in the high-dimensional RIS cascade channel. According to the needs of the M step, the diagonal vector of Σ is first obtained using the diagonal estimation criterion. The diagonal estimation rule is as follows:

[0156] set up Square, set p K is a random probe vector where each entry is independently distributed such that Use Rademacher distribution to characterize the random probe vector p k , each p k,t The probability of taking {-1, +1} is equal, for p k , let d l =Mp k , each considering a diagonal estimator For all t=1,2,…,U,

[0157]

[0158] Among them, each s t It is for M t,t is an unbiased estimate of . Therefore, applying Σ to the diagonal estimation rule, the diagonal vector It can be expressed as:

[0159]

[0160] In order to avoid covariance inversion, this embodiment converts formulas (26) and (29) into a form for solving a linear system solution. Formulas (26) and (29) are respectively re-expressed as:

[0161]

[0162] The preconditioned conjugate gradient (PCG) algorithm is used to solve the linear system AX = B in parallel, where and They are as follows:

[0163]

[0164] However, for high-dimensional problems, since the condition number of the matrix is ​​large, more calculation steps are required. In this embodiment, the diagonal preprocessing matrix M=diag{Σ -1} can accelerate convergence. The linear system solution obtained is divided into two parts, represented by μ = X(:, 1) and Therefore, Σ t The calculation formula is:

[0165]

[0166] Using the above results, the M-step of the EM algorithm estimates the hyperparameters γ and β by maximizing the log-marginal likelihood distribution, resulting in the following update results:

[0167]

[0168] in The time complexity of PCG is Reduce to Where S is the number of PCG steps, K is the number of detection vectors, It is an application time.

[0169] According to another aspect of the embodiment of the present application, a two-stage cascade channel estimation device is provided, such as Figure 4 As shown, the device comprises:

[0170] A compressed sensing module 101 is used to perform compressed sensing on the constructed initial system model to obtain a communication system model, wherein the initial system model includes a two-stage cascade channel from a user to a reconfigurable smart surface side channel and from a reconfigurable smart surface side to a base station side channel;

[0171] A first calculation module 102, configured to calculate a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the initial system model;

[0172] An interval determination module 103 is used to determine, based on the grid index value, an index interval to which an arrival angle belongs, where the arrival angle is an arrival angle of a signal received by the base station side;

[0173] An updating module 104, configured to update the sparse measurement matrix based on the index interval to obtain a sparse representation matrix;

[0174] The second calculation module 105 is used to obtain a compressed cascade channel matrix based on the sparse representation matrix and the cascade channel matrix in the communication system model.

[0175] In one or more embodiments, the compressed sensing module 101 is used to:

[0176] Based on a first dictionary matrix and a second dictionary matrix of discrete grid points generated by the initial system model, updating the cascaded channels in the initial system model, the first dictionary matrix being a uniform discrete array of arrival angles on the base station side, and the second dictionary matrix being a uniform discrete array of arrival angles on the reconfigurable smart surface side;

[0177] Based on the updated cascade channel, the initial system model is rewritten to obtain the communication system model.

[0178] In one or more embodiments, the interval determination module 103 is used to:

[0179] Based on the grid index value, obtain the right adjacent index value and the left adjacent index value of the grid index value;

[0180] Calculating a first channel coefficient corresponding to the left adjacent index value and a second channel coefficient corresponding to the right adjacent index value;

[0181] When the first channel coefficient is greater than or equal to the second channel coefficient, determining that the index interval to which the arrival angle belongs is an interval consisting of the left adjacent index value and the grid index value;

[0182] When the first channel coefficient is less than the second channel coefficient, it is determined that the index interval to which the arrival angle belongs is an interval consisting of the grid index value and the right adjacent index value.

[0183] In one or more embodiments, the update module 104 is used to:

[0184] Selecting sample parameters in the index interval, and obtaining a support set of the angle of arrival at the base station side based on the sample parameters;

[0185] Calculating an array vector based on the support set and a first dictionary matrix of discrete grid points generated by the initial system model;

[0186] Based on the array vector, a sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix.

[0187] In one or more embodiments, the second calculation module 105 is used to:

[0188] Based on the sparse representation matrix, reducing the dimension of the cascade channel matrix in the communication system model to obtain a pattern formula including the compressed cascade channel matrix;

[0189] The compressed cascade channel matrix is ​​calculated based on an expectation-maximization algorithm and the mode formula.

[0190] In one or more embodiments, the second calculation module 105 is further configured to:

[0191] Modeling the compressed cascade channel matrix based on an expectation-maximization algorithm and the pattern formula to obtain a modeling formula for the compressed cascade channel matrix;

[0192] Based on the modeling formula, a calculation formula for a first a posteriori parameter is obtained, where the first a posteriori parameter is a mean value of a random variable of the compressed cascade channel matrix;

[0193] Deforming the calculation formula of the first a posteriori parameter so that the second a posteriori parameter in the calculation formula is moved to the other side of the equal sign; and, according to the diagonal estimation criterion, obtaining the calculation formula of the diagonal vector of the second a posteriori parameter, deforming the calculation formula of the diagonal vector so that the second a posteriori parameter is moved to the other side of the equal sign in the calculation formula of the diagonal vector, wherein the second a posteriori parameter is the covariance of the random variable of the compressed cascade channel matrix;

[0194] Calculate the first a posteriori parameter based on the expectation-maximization algorithm, the calculation formula of the first a posteriori parameter after deformation, and the calculation formula of the diagonal vector after deformation;

[0195] The compressed cascade channel matrix is ​​calculated based on the first a posteriori parameter and the mode formula.

[0196] The two-stage cascade channel estimation device provided in the embodiment of the present application and the two-stage cascade channel estimation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0197] The present application also provides a computer device to perform the above two-stage cascade channel estimation method. Figure 5 It shows a schematic diagram of a computer device provided by some embodiments of the present application. Figure 5 As shown, the computer device 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, wherein the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and when the processor 800 runs the computer program, the two-stage cascade channel estimation method provided in any of the aforementioned embodiments of the present application is executed.

[0198] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 803 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0199] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store a program, and the processor 800 executes the program after receiving an execution instruction. The two-stage cascade channel estimation method disclosed in any implementation of the embodiment of the present application may be applied to the processor 800, or implemented by the processor 800.

[0200] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 800. The above processor 800 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-made programmable gate array (FPTA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and completes the steps of the above method in combination with its hardware.

[0201] The computer device provided in the embodiment of the present application and the two-stage cascade channel estimation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0202] An embodiment of the present application also provides a computer-readable storage medium corresponding to the two-stage cascade channel estimation method provided in the aforementioned embodiment. The computer-readable storage medium is a CD, on which a computer program (i.e., a computer program product) is stored. When the computer program is executed by a processor, the two-stage cascade channel estimation method provided in any of the aforementioned embodiments will be executed.

[0203] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0204] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the two-stage cascade channel estimation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0205] The present application also provides a computer program product. Figure 6 The computer program product 600 carries a program code, namely a computer program 601. The instructions included in the computer program 601 can be used to execute the steps of the two-stage cascade channel estimation method described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0206] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0207] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0208] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0209] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0210] It should also be noted that in the system and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0211] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the techniques of the teachings defined by the appended claims. Furthermore, the scope of the claims of this application is not limited to the specific aspects of the processes, machines, manufactures, compositions, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions, means, methods, or actions within their scope.

[0212] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0213] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A two-stage cascade channel estimation method, characterized in that: include: Performing compressed sensing on the constructed initial system model to obtain a communication system model, wherein the initial system model includes a two-stage cascade channel from a user to a reconfigurable smart surface side and from the reconfigurable smart surface side to a base station side; Calculating a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the initial system model; Based on the grid index value, determine the index interval to which the angle of arrival belongs, where the angle of arrival is the angle of arrival of the signal received by the base station side; Based on the index interval, the sparse measurement matrix is ​​updated to obtain a sparse representation matrix; A compressed concatenated channel matrix is ​​obtained based on the sparse representation matrix and the concatenated channel matrix in the communication system model.

2. The two-stage cascade channel estimation method according to claim 1, characterized in that: Compressed sensing is performed on the constructed initial system model to obtain the communication system model, including: Based on a first dictionary matrix and a second dictionary matrix of discrete grid points generated by the initial system model, updating the cascaded channels in the initial system model, the first dictionary matrix being a uniform discrete array of arrival angles on the base station side, and the second dictionary matrix being a uniform discrete array of arrival angles on the reconfigurable smart surface side; Based on the updated cascade channel, the initial system model is rewritten to obtain the communication system model.

3. The two-stage cascade channel estimation method according to claim 1, characterized in that: Determining, based on the grid index value, an index interval to which the arrival angle belongs, includes: Based on the grid index value, obtain the right adjacent index value and the left adjacent index value of the grid index value; Calculating a first channel coefficient corresponding to the left adjacent index value and a second channel coefficient corresponding to the right adjacent index value; When the first channel coefficient is greater than or equal to the second channel coefficient, determining that the index interval to which the arrival angle belongs is an interval consisting of the left adjacent index value and the grid index value; When the first channel coefficient is less than the second channel coefficient, it is determined that the index interval to which the arrival angle belongs is an interval consisting of the grid index value and the right adjacent index value.

4. The two-stage cascade channel estimation method according to claim 1, characterized in that: Based on the index interval, a sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix, including: Selecting sample parameters in the index interval, and obtaining a support set of the angle of arrival at the base station side based on the sample parameters; Calculating an array vector based on the support set and a first dictionary matrix of discrete grid points generated by the initial system model; Based on the array vector, a sparse measurement matrix in the communication system model is updated to obtain a sparse representation matrix.

5. The two-stage cascade channel estimation method according to claim 1, characterized in that: Based on the sparse representation matrix and the cascaded channel matrix in the communication system model, a compressed cascaded channel matrix is ​​obtained, comprising: Based on the sparse representation matrix, reducing the dimension of the cascade channel matrix in the communication system model to obtain a pattern formula including the compressed cascade channel matrix; The compressed cascade channel matrix is ​​calculated based on an expectation-maximization algorithm and the mode formula.

6. The two-stage cascade channel estimation method according to claim 5, characterized in that: Based on the expectation maximization algorithm and the mode formula, the compressed cascade channel matrix is ​​calculated, including: Modeling the compressed cascade channel matrix based on an expectation-maximization algorithm and the pattern formula to obtain a modeling formula for the compressed cascade channel matrix; Based on the modeling formula, a calculation formula for a first a posteriori parameter is obtained, where the first a posteriori parameter is a mean value of a random variable of the compressed cascade channel matrix; Deforming the calculation formula of the first a posteriori parameter so that the second a posteriori parameter in the calculation formula is moved to the other side of the equal sign; and, according to the diagonal estimation criterion, obtaining the calculation formula of the diagonal vector of the second a posteriori parameter, deforming the calculation formula of the diagonal vector so that the second a posteriori parameter is moved to the other side of the equal sign in the calculation formula of the diagonal vector, wherein the second a posteriori parameter is the covariance of the random variable of the compressed cascade channel matrix; Calculate the first a posteriori parameter based on the expectation-maximization algorithm, the calculation formula of the first a posteriori parameter after deformation, and the calculation formula of the diagonal vector after deformation; The compressed cascade channel matrix is ​​calculated based on the first a posteriori parameter and the mode formula.

7. A two-stage cascade channel estimation device, characterized in that: include: A compressed sensing module is used to perform compressed sensing on the constructed initial system model to obtain a communication system model, wherein the initial system model includes a two-stage cascade channel from a user to a reconfigurable smart surface side channel and from a reconfigurable smart surface side channel to a base station side channel; A first calculation module, configured to calculate a grid index value based on a base station measurement matrix and a sparse measurement matrix included in the initial system model; An interval determination module, configured to determine, based on the grid index value, an index interval to which an angle of arrival belongs, wherein the angle of arrival is an angle of arrival of a signal received by the base station side; An updating module, used for updating the sparse measurement matrix based on the index interval to obtain a sparse representation matrix; The second calculation module is used to obtain a compressed cascade channel matrix based on the sparse representation matrix and the cascade channel matrix in the communication system model.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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