Multi-RIS assisted semi-blind joint channel estimation and symbol detection method and system

Abstract

The present invention provides a multi-RIS-assisted semi-blind joint channel estimation and symbol detection method and system. The solution includes establishing a multi-RIS-assisted MIMO communication system model; establishing a signal received by a base station as a parallel factor tensor model; converting the channel estimation and symbol detection problem into an iterative optimization problem of a cost function based on a tensor expansion; and solving the cost function through ALS. The solution solves the problem of excessively high dimensionality of received signals due to the multiplication of the number of RIS reflection elements in multi-RIS-assisted communication scenarios, making it difficult to accurately and quickly obtain channel state information. Compared with the pilot-assisted ALS algorithm and the KAKF semi-blind receiver in single-RIS-assisted communication scenarios, the method can effectively improve estimation accuracy and reduce bit error rate and pilot overhead.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a multi-RIS assisted semi-blind joint channel estimation and symbol detection method and system. Background Art

[0002] Reconfigurable smart reflective surfaces (RIS) are considered a candidate technology for future sixth-generation (6G) wireless communications due to their superior performance, including low power consumption, low latency, high energy efficiency, high speed, and ability to achieve large-scale connectivity. RIS is a planar surface composed of a large number of passive (or semi-passive) controllable reflective elements, each of which can independently adjust the phase of the incident signal. If channel state information (CSI) is known, the RIS can appropriately adjust the reflection coefficients at different reflective elements based on the CSI, thereby constructively increasing the useful signal and reducing the interference signal at the receiver. Although RIS can bring gain to wireless communication systems, a completely passive RIS does not have the ability to independently process signals. Therefore, how to quickly and accurately obtain CSI in a RIS-assisted system is a challenge.

[0003] In recent years, some studies have utilized tensor-based methods to address the CE (channel estimation) problem in single RIS-assisted communication systems. For example, L. Wei et al. proposed a tensor-based alternating least squares (ALS) CE algorithm. This algorithm uses tensor decomposition to reduce the dimensionality of high-dimensional data. However, excessive pilot overhead significantly reduces its spectral efficiency. To balance pilot overhead and CE accuracy, recent research has turned its attention to semi-blind estimation. G.T. De Araujo et al. proposed a tensor-based two-stage semi-blind KAKF receiver for jointly estimating the channel and symbol matrix, eliminating the pilot training stage. Although the KAKF algorithm does not require any iterations and has low computational complexity, it suffers from error propagation between the two estimation stages.

[0004] The inventors discovered that the above-mentioned solutions are applicable to single-RIS-assisted systems. However, new challenges arise in multiple (greater than two) RIS-assisted multiple-input, multiple-output (MIMO) systems. Due to the large number of RIS reflection surfaces deployed in a multi-RIS-assisted system, the system contains many channels and a complex composition. At the same time, the number of elements equipped with each RIS reflection surface is also very large, resulting in a surge in the dimension of the channel matrix. If a single RIS-assisted channel estimation solution is used for estimation, the base station needs to simultaneously process all received signals reflected by the RIS to complete the estimation of all channels, which is time-consuming and labor-intensive. At the same time, if a pilot-assisted channel estimation method is used, it will generate huge pilot overhead, reducing spectral efficiency. Therefore, how to solve the CE problem in systems with two or more RISs to accurately and quickly obtain CSI has become an urgent problem that needs to be solved. Summary of the Invention

[0005] The embodiments of the present invention provide a multi-RIS assisted semi-blind joint channel estimation and symbol detection method and system to solve the problem that the prior art cannot solve the channel estimation in a system with two or more RISs, thereby accurately and quickly obtaining channel state information.

[0006] According to a first aspect of an embodiment of the present invention, a multi-RIS-assisted semi-blind joint channel estimation and symbol detection method is provided, comprising:

[0007] Construct a multi-RIS-assisted MIMO communication system model, wherein multiple RIS are divided into several subclusters according to preset conditions;

[0008] Based on the constructed multi-RIS-assisted MIMO communication system model, the noise-free received signal of the base station is constructed as a parallel factor tensor model; the parallel factor tensor model is cut along horizontal and vertical slices through balanced factor decomposition, and a first cost function and a second cost function are constructed based on the cutting results; and a symbol matrix estimate and a coupling channel matrix estimate are obtained by alternately optimizing the first cost function and the second cost function;

[0009] Based on the symbol matrix estimate, a base station received signal is reconstructed to obtain a reconstructed received signal; the reconstructed noise-free received signal is constructed as a parallel factor tensor model, and the parallel factor tensor model is cut along horizontal and vertical planes through balanced factor decomposition, and a third cost function and a fourth cost function are constructed based on the cutting results; and an estimated value of a channel matrix is ​​obtained by alternately optimizing the third cost function and the fourth cost function, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

[0010] Furthermore, based on the constructed multi-RIS-assisted MIMO communication system model, the noise-free received signal of the base station is constructed as a parallel factor tensor model, which specifically includes: establishing a mathematical model of the received signal at the base station end based on the constructed multi-RIS-assisted MIMO communication system model; based on the mathematical model, by fixing the number of RIS deployed in the sub-cluster, only considering the impact of a preset number of transmission time blocks on the received signal at the base station end, thereby realizing the construction of the parallel factor tensor model.

[0011] Furthermore, the mathematical model of the base station receiving signal is specifically expressed as follows:

[0012] Y p,k =GD p (E)HD k (W)X T +B[p,k]

[0013] Where G is the channel from the base station to the RIS subcluster, H is the channel from the RIS subcluster to the user, X is the transmission symbol matrix, E is the RIS phase shift matrix, W is the coding matrix, B is the additive white Gaussian noise matrix, P is the number of RIS deployed in the subcluster, p = 1, ..., P, K is the number of transmission time blocks, k = 1, ..., K; D p (E) is the diagonal matrix operator of the RIS phase shift matrix, which means placing the pth row of the matrix E on the main diagonal of a diagonal matrix, D k (W) is the diagonal matrix operator of the encoding matrix, which means placing the kth row of the matrix W on the main diagonal of a diagonal matrix.

[0014] Furthermore, the first cost function and the second cost function are constructed based on the cutting result, which are specifically expressed as follows:

[0015]

[0016] Where X is the symbol matrix, F is the coupling channel matrix, is the symbolic matrix estimate, is the estimated value of the coupling channel matrix, and Can be regarded as tensors Horizontal and side slices of the plot, ⊙ is Khatri Rao.

[0017] Furthermore, based on the symbol matrix estimation value, the base station received signal is reconstructed to obtain the reconstructed received signal, specifically: a pseudo-inverse operation is performed on the symbol matrix estimation value to obtain a generalized inverse matrix, an inverse operation is performed on the diagonal matrix operator of the coding matrix to obtain an inverse matrix, and the generalized inverse matrix and the inverse matrix of the diagonal matrix operator of the coding matrix are multiplied with the base station received signal to obtain the reconstructed received signal.

[0018] Furthermore, the third cost function and the fourth cost function are constructed based on the cutting results, which are specifically expressed as follows:

[0019]

[0020] Among them, Q is the equivalent matrix of the RIS sub-cluster to user channel matrix, G is the user to RIS sub-cluster channel matrix, E is the RIS phase shift matrix, is the estimated value of the channel matrix, and Tensors horizontal and side slices.

[0021] Furthermore, the multiple RISs are divided into several subclusters according to preset conditions, and the specific division satisfies the following relationship:

[0022]

[0023] Where P is the number of RIS deployed in a sub-cluster, and P≥2, j=2,…,P, x1 is the distance from the base station to the first RIS, x j is the distance from the base station to the jth RIS, y1 is the distance from the user to the first RIS, y j is the distance from the user to the jth RIS, d j is the distance from the first RIS to the jth RIS, θ j is the angle between the path from the base station to the first RIS and the path from the base station to the jth RIS, is the angle between the user’s path to the first RIS and the user’s path to the jth RIS, τ and λ are the deployment angle thresholds, ω1 and ω j are the angles between the first and j-th RIS planes and the X-axis of the reference coordinate system with the base station as the origin, α1 and α j are the angles between the first and j-th RIS planes and the Y axis of the reference coordinate system with the base station as the origin, β1 and β j are the angles between the first and j-th RIS planes and the Z axis of the reference coordinate system with the base station as the origin.

[0024] According to a second aspect of an embodiment of the present invention, a multi-RIS-assisted semi-blind joint channel estimation and symbol detection system is provided, comprising:

[0025] A system model building unit, which is used to build a MIMO communication system model assisted by multiple RISs; wherein the multiple RISs are divided into a number of subclusters according to preset conditions;

[0026] A symbol matrix estimation unit is configured to construct a noise-free received signal of a base station into a parallel factor tensor model based on the constructed multi-RIS-assisted MIMO communication system model; cut the parallel factor tensor model along horizontal and vertical slices through balanced factor decomposition, and construct a first cost function and a second cost function based on the cutting results; and obtain a symbol matrix estimate and a coupling channel matrix estimate by alternately optimizing the first cost function and the second cost function;

[0027] A channel matrix estimation unit is configured to reconstruct a base station received signal based on the symbol matrix estimate to obtain a reconstructed received signal; construct the reconstructed noise-free received signal into a parallel factor tensor model, slice the parallel factor tensor model along horizontal and vertical planes through balanced factor decomposition, and construct a third cost function and a fourth cost function based on the slice results; and obtain an estimated value of the channel matrix by alternately optimizing the third and fourth cost functions, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

[0028] According to a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored and running on the memory, wherein when the processor executes the program, the method for semi-blind joint channel estimation and symbol detection assisted by multiple RIS is implemented.

[0029] According to a fourth aspect of an embodiment of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, it implements the multi-RIS-assisted semi-blind joint channel estimation and symbol detection method.

[0030] One or more of the above technical solutions have the following beneficial effects:

[0031] (1) The present invention provides a multi-RIS-assisted semi-blind joint channel estimation and symbol detection method and system. The scheme establishes a multi-RIS-assisted MIMO communication system model, establishes the signal received by the base station as a parallel factor tensor model, transforms the channel estimation and symbol detection problem into an iterative optimization problem of the cost function based on the tensor expansion, and solves the cost function by least squares (ALS), thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection. The scheme solves the problem that in multi-RIS-assisted communication scenarios, the dimension of the received signal is too high due to the doubling of the number of RIS reflection elements, making it difficult to accurately and quickly obtain channel state information. Compared with the pilot-assisted ALS algorithm and the KAKF semi-blind receiver in single-RIS-assisted communication scenarios, the method can effectively improve the estimation accuracy and reduce the bit error rate and pilot overhead.

[0032] (2) The solution of the present invention divides multiple RISs into multiple subclusters according to conditions, eliminating the need to estimate multiple RISs with similar channels separately. Instead, it only needs to estimate multiple similar channels once, saving computational costs. At the same time, by utilizing the dimensionality reduction and noise reduction properties of parallel factor decomposition, the channel estimation and symbol detection problems are transformed into four cost functions for iterative solution, thereby improving estimation accuracy and reducing bit error rate.

[0033] (3) The solution described in the present invention is based on the ALS algorithm and is used to solve the channel estimation problem of multi-RIS assisted systems. Simulation results show that under the same signal-to-noise ratio, the proposed semi-blind joint channel estimation and symbol detection algorithm is superior to the pilot-assisted ALS solution and the KAKF semi-blind receiver solution in a single RIS assisted scenario, effectively improving the estimation accuracy while reducing the pilot overhead.

[0034] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 1 is a block diagram of a MIMO communication system assisted by multiple intelligent reflecting surfaces according to an embodiment of the present invention;

[0037] Figure 2 Flowchart of the multi-RIS-assisted semi-blind joint channel estimation and symbol detection method described in an embodiment of the present invention;

[0038] Figure 3 A RIS deployment azimuth diagram for a MIMO communication system assisted by multiple intelligent reflecting surfaces according to an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of RIS deployment angles in a MIMO communication system assisted by multiple intelligent reflecting surfaces according to an embodiment of the present invention;

[0040] Figure 5 This figure compares the simulation curves of the normalized mean square error (NMSE) of the KAKF semi-blind receiver for the concatenated channel versus the signal-to-noise ratio (SNR) for the multi-RIS-assisted semi-blind joint channel estimation and symbol detection method described in an embodiment of the present invention and the pilot-assisted ALS algorithm in a single RIS-assisted scenario;

[0041] Figure 6 This figure compares the simulation curves of the bit error rate (SER) of the symbol matrix of the KAKF semi-blind receiver with the signal-to-noise ratio (SNR) of the multi-RIS-assisted semi-blind joint channel estimation and symbol detection method described in an embodiment of the present invention and the pilot-assisted ALS algorithm in a single RIS-assisted scenario. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0044] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0045] like Figure 2 As shown, the embodiment of the present invention provides a multi-RIS assisted semi-blind joint channel estimation and symbol detection method, which specifically includes the following processing procedures:

[0046] Step 1: Construct a multi-RIS-assisted MIMO communication system model; wherein multiple RIS are divided into several subclusters according to preset conditions;

[0047] In a specific implementation, the communication system used is a multi-RIS assisted end-to-end MIMO system. In this embodiment, the initial parameters are set as follows (it can be understood that in a specific implementation, the following parameters can be set according to actual needs): the number of base station transmitting antennas M is 5, the number of RIS in the sub-cluster P is 32, the number of reflection units in each RIS is 32, the number of user antennas L is 2, the deployment angle thresholds τ and λ are both set to 10°, and the RIS deployment angle in the sub-cluster is set to ω1=ω j =α1=α j =β1=β j =30°, (j=2,…,P), the distance from the base station to the first RIS is set to 1km, and the distance from the base station to the jth RIS is set to x j ∈(750,1500)m, the distance from the user to the first RIS is set to 1km, and the distance from the user to the jth RIS is set to y j =x j , the distance between the jth RIS and the first RIS is set to The number of transmission time blocks K in a transmission frame is 128, and each transmission time block contains 5 time slots. The transmitted data symbols are selected from the 16PSK alphabet. The coding matrix and RIS phase matrix are taken from the first P rows and K rows of the N×N and L×L discrete Fourier transform matrices respectively. The system block diagram is shown in Figure 1 shown.

[0048] Specifically, the multiple RISs are divided into several subclusters according to preset conditions, and the specific division satisfies the following relationship:

[0049]

[0050] Where P is the number of RIS deployed in a sub-cluster, and P≥2, j=2,…,P, x1 is the distance from the base station to the first RIS, x j is the distance from the base station to the jth RIS, y1 is the distance from the user to the first RIS, y j is the distance from the user to the jth RIS, d j is the distance from the first RIS to the jth RIS, θ j is the angle between the path from the base station to the first RIS and the path from the base station to the jth RIS, is the angle between the user’s path to the first RIS and the user’s path to the jth RIS, τ and λ are the deployment angle thresholds, ω1 and ω j are the angles between the first and j-th RIS planes and the X-axis of the reference coordinate system with the base station as the origin, α1 and α j are the angles between the first and j-th RIS planes and the Y axis of the reference coordinate system with the base station as the origin, β1 and β j are the angles between the first and j-th RIS planes and the Z axis of the reference coordinate system with the base station as the origin.

[0051] Among them, such as Figure 3 As shown, the RIS deployment orientation diagram in the MIMO communication system assisted by multiple intelligent reflecting surfaces is shown; Figure 4 A schematic diagram of RIS deployment angles in a MIMO communication system assisted by multiple intelligent reflecting surfaces is presented. Direct signals are not considered. Furthermore, since the RIS deployment angles within each subcluster are consistent, direct signals between RISs are not considered. The system's transceiver channels are divided into two subchannels: the base station-RIS subcluster G and the RIS subcluster-user H. Both subchannels employ a Rayleigh fading channel model, meaning that G and H have independent and identically distributed complex Gaussian terms.

[0052] Step 2: Based on the constructed multi-RIS-assisted MIMO communication system model, the noise-free received signal of the base station is constructed as a parallel factor tensor model; the parallel factor tensor model is cut along horizontal and vertical slices through balanced factor decomposition, and a first cost function and a second cost function are constructed based on the cutting results; by alternately optimizing the first cost function and the second cost function, a symbol matrix estimate and a coupling channel matrix estimate are obtained;

[0053] In a specific implementation, the step 2 specifically includes the following processing steps:

[0054] Step 201: Establish a mathematical model of the signal received by the base station. Specifically, assume a transmission protocol with a coherence time T A =T E +T B And TB Much smaller than T A , where T E and T B Represent the data transmission phase and the semi-blind estimation phase respectively. During the semi-blind estimation period, T B is divided into K time blocks, each time block includes T time slots, then T B =KT<<T A . By considering the uplink communication scenario, with the help of p RIS in the sub-cluster, the base station receives M independent data streams encoded by the user. The discrete-time signal vector received by the BS (Base Station) in the tth (t=1,...,T)th time slot of the kth (k=1,...,K)th time block reflected with the pth (p=1,...,P)th RIS phase configuration can be expressed as: y[p,k,t]=Gdiag(e[p])Hdiag(w[k,t])x[t]+b[p,k,t], where x[t] is the transmission symbol vector, e[p] is the pth RIS phase vector, and w[k,t] is the coding vector, which is time-varying between blocks but constant within each block, i.e. w[k,t]=w[k],1≤t≤T, and b[p,k,t] is the additive white Gaussian noise vector. Based on the above assumptions, we collect the received signals in the T time slots of each time block and obtain: Y p,k =GD p (E)HD k (W)X T +B[p,k], where G is the base station-RIS subcluster channel, H is the RIS subcluster-user channel, X is the transmission symbol matrix, E is the RIS phase shift matrix, W is the coding matrix, B is the additive white Gaussian noise matrix, P (p = 1, ..., P) is the number of RIS deployed in the subcluster, K (k = 1, ..., K) is the number of transmission time blocks in a transmission frame, and the RIS phase shift matrix is Among them, the elements in the matrix E represent the RIS phase vector, and the encoding matrix Among them, the elements in the matrix W represent the encoding vectors, Among them, the elements in the matrix X represent the transmission symbol vector, and the vector Indicates that the mean is 0 and the variance is σ 2 The additive white Gaussian noise matrix of , where the elements in the matrix B[p,k] represent the additive white Gaussian noise vector.

[0055] Step 202: Build the noise-free received signal in step 201 into a parallel factor tensor model. We define F = GD p(E)H, by fixing the number of RIS deployments P, only considering the impact of k transmission time blocks on the base station's received signal. For the convenience of calculation, let p = 1, and rewrite the noise-free received signal in step 2 as: Y k =FD k (W)X T , then the received signal In parallel factorization, it can be viewed as a three-dimensional tensor The kth vertical slice of , where M is the number of antennas equipped with the base station.

[0056] Step 203: Based on the parallel factor decomposition, the tensor model in step 202 is split to obtain the expanded mode-1: and mode-2: and Can be regarded as tensors horizontal, side slices.

[0057] Step 204: Establish the cost function of the i+1th iteration based on the expansion mode-1 and mode-2 of step 203 The expression is:

[0058]

[0059] Step 205: For the cost function in step 204 and Perform alternating optimization. Initialize the number of iterations, given The initial value and iteration termination threshold of Solving the cost function Get the closed-form solution of the symbolic matrix Fix the variables again Solving the cost function The closed-form solution of the coupling channel matrix is ​​obtained The solution is solved alternately in this way until the iteration termination threshold is met and the loop is exited. The closed-form solution obtained in the last iteration is the estimated value of the symbol matrix. and the estimated value of the coupling channel matrix

[0060] In the specific implementation, let the number of iterations i = 1, initialize The iteration termination threshold is set to ε α =10 -5 , first Substitute into the cost function Solve to get the symbolic matrix closed-form solution Then Substitute into the cost function The closed-form solution of the coupling channel matrix is ​​obtained Iteration number i+1, alternately solve the two cost functions again. The iteration terminates when Represents the reconstruction error of the i+1th iteration, and the estimated value of the symbol matrix is ​​obtained at the end of the i+1th iteration and the estimated value of the coupling channel matrix (It should be noted that this value will not be used in subsequent calculations and can be ignored).

[0061] Step 3: Based on the symbol matrix estimate, reconstruct the base station received signal to obtain a reconstructed received signal; construct the reconstructed noise-free received signal into a parallel factor tensor model, and cut the parallel factor tensor model along horizontal and vertical planes through balanced factor decomposition, and construct a third cost function and a fourth cost function based on the cutting results; obtain an estimate of the channel matrix by alternately optimizing the third cost function and the fourth cost function, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

[0062] In a specific implementation, step 3 specifically includes the following processing:

[0063] Step 301: Since the estimated value of the transmission symbol matrix is ​​obtained in the above step 205 Therefore, the transmission symbol matrix X and the coding matrix W can be removed based on the estimated value, and the symbol matrix estimated value in step 205 can be calculated respectively. Perform pseudo-inverse operation to obtain the generalized inverse matrix To D k (W) performs inverse operation to obtain the inverse matrix D k (W) -1 ,use and D k (W) -1 The base station receives the signal Y in step 201 p,k Multiply to get the reconstructed received signal

[0064] Step 302: The noise-free received signal in step 301 is established as a parallel factor tensor model. We define By fixing the number of variable transmission time blocks K, only the impact of the deployed p RIS on the base station end receiving signal is considered. For the convenience of calculation, let k = 1. The expression of the denoised receiving signal is converted from the form in step 301 to Y p =GD p (E)Q T , then the received signal In parallel factorization, it can be viewed as a three-dimensional tensor The pth vertical section of .

[0065] Step 303: Based on parallel factor decomposition, the tensor model in step 302 is split to obtain the expanded form mode-1: and mode-2: and Can be regarded as tensors horizontal, side slices.

[0066] Step 304: Establish the cost function for the i+1th iteration based on the expansion mode-1 and mode-2 of step 303 The expression is:

[0067]

[0068] Step 305: For the cost function in step 304 and Perform alternating optimization.

[0069] Initialize the number of iterations, given The initial value and iteration termination threshold of Solving the cost function Get the symbolic matrix closed form Fix the variables again Solving the cost function The closed-form solution of the coupling channel matrix is ​​obtained The solution is solved alternately until the iteration termination threshold is met and the loop is exited. The closed-form solution obtained in the last iteration is the estimated value of the channel matrix. and

[0070] In the specific implementation, let the number of iterations i = 1, initialize The iteration termination threshold is set to ε β =10 -5 , first Substitute into the cost function Solve to get the symbolic matrix closed-form solution Then Substitute into the cost function The closed-form solution of the coupling channel matrix is ​​obtained Iteration number i+1, alternately solve the two cost functions again. The iteration terminates when Represents the reconstruction error of the i+1th iteration, and the estimated value of the channel matrix is ​​obtained at the end of the i+1th iteration and

[0071] Furthermore, in order to verify the effectiveness of the solution described in this embodiment, the following relevant experimental verifications were carried out:

[0072] like Figure 5 As shown in FIG, the multi-RIS assisted semi-blind joint channel estimation and symbol detection method described in this embodiment is compared with the pilot-assisted ALS algorithm in a single RIS assisted scenario, and the simulation curve comparison of the normalized mean square error (NMSE) of the KAKF semi-blind receiver on the cascade channel as a function of the signal-to-noise ratio (SNR). Figure 5 It can be seen that the number of RIS deployed by the scheme proposed in the present invention is much larger than that of the pilot-assisted ALS scheme and the KAKF semi-blind receiver scheme in the single-RIS assisted scenario. When other parameters are the same, the scheme proposed in the present invention is still superior to the pilot-assisted ALS scheme and the KAKF semi-blind receiver scheme in the single-RIS assisted scenario. The scheme proposed in the present invention can not only estimate more channels simultaneously, but also improve the estimation accuracy.

[0073] like Figure 6 The figure shows a comparison of the simulation curves of the bit error rate (SER) of the symbol matrix of the KAKF semi-blind receiver with the signal-to-noise ratio (SNR) of the multi-RIS assisted semi-blind joint channel estimation and symbol detection method described in this embodiment and the pilot-assisted ALS algorithm in a single RIS assisted scenario. Figure 6 It can be seen that the number of RIS deployed by the solution described in this embodiment is much greater than that of the pilot-assisted ALS solution and the KAKF semi-blind receiver solution in the single RIS assistance scenario. When other parameters are the same, the solution proposed in this embodiment reduces the bit error rate by nearly two orders of magnitude compared to the pilot-assisted ALS solution and the KAKF semi-blind receiver solution in the single RIS assistance scenario.

[0074] In one or more embodiments, corresponding to the above-mentioned multi-RIS-assisted semi-blind joint channel estimation and symbol detection method, a multi-RIS-assisted semi-blind joint channel estimation and symbol detection system is provided, including:

[0075] A system model building unit, which is used to build a MIMO communication system model assisted by multiple RISs; wherein the multiple RISs are divided into a number of subclusters according to preset conditions;

[0076] A symbol matrix estimation unit is configured to construct a noise-free received signal of a base station into a parallel factor tensor model based on the constructed multi-RIS-assisted MIMO communication system model; cut the parallel factor tensor model along horizontal and vertical slices through balanced factor decomposition, and construct a first cost function and a second cost function based on the cutting results; and obtain a symbol matrix estimate and a coupling channel matrix estimate by alternately optimizing the first cost function and the second cost function;

[0077] A channel matrix estimation unit is configured to reconstruct a base station received signal based on the symbol matrix estimate to obtain a reconstructed received signal; construct the reconstructed noise-free received signal into a parallel factor tensor model, slice the parallel factor tensor model along horizontal and vertical planes through balanced factor decomposition, and construct a third cost function and a fourth cost function based on the slice results; and obtain an estimated value of the channel matrix by alternately optimizing the third and fourth cost functions, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

[0078] It can be understood that the system is consistent with the method in the above embodiment, and its specific details are described in detail in the method embodiment, so they are not repeated here.

[0079] In further embodiments, there is also provided:

[0080] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the above embodiment. For the sake of brevity, no further details are given here.

[0081] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0082] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0083] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in the above embodiment is completed.

[0084] The methods in the above embodiments can be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules within the processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in a memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods. To avoid repetition, a detailed description is not given here.

[0085] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in this embodiment can be implemented using electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0086] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A multi-RIS assisted semi-blind joint channel estimation and symbol detection method, characterized in that: include: Construct a multi-RIS-assisted MIMO communication system model, wherein multiple RIS are divided into several subclusters according to preset conditions; Based on the constructed multi-RIS-assisted MIMO communication system model, the noise-free received signal of the base station is constructed as a parallel factor tensor model; the parallel factor tensor model is cut along horizontal and vertical slices through balanced factor decomposition, and a first cost function and a second cost function are constructed based on the cutting results; and a symbol matrix estimate and a coupling channel matrix estimate are obtained by alternately optimizing the first cost function and the second cost function; Based on the symbol matrix estimate, a base station received signal is reconstructed to obtain a reconstructed received signal; the reconstructed received signal is constructed as a parallel factor tensor model, and the parallel factor tensor model is cut along horizontal and vertical planes through balanced factor decomposition, and a third cost function and a fourth cost function are constructed based on the cutting results; and an estimated value of a channel matrix is ​​obtained by alternately optimizing the third cost function and the fourth cost function, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

2. A multi-RIS assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 1, characterized in that: The method, based on the constructed multi-RIS-assisted MIMO communication system model, constructs the noise-free received signal of the base station into a parallel factor tensor model. The method specifically includes: establishing a mathematical model of the received signal at the base station end based on the constructed multi-RIS-assisted MIMO communication system model; based on the mathematical model, by fixing the number of RIS deployed in the sub-cluster and only considering the impact of a preset number of transmission time blocks on the received signal at the base station end, thereby realizing the construction of the parallel factor tensor model.

3. A multi-RIS-assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 2, characterized in that: The mathematical model of the base station receiving signal is specifically expressed as follows: Y p,k =GD p (E)HD k (W)X T +B[p,k] Where G is the channel from the base station to the RIS subcluster, H is the channel from the RIS subcluster to the user, X is the transmission symbol matrix, E is the RIS phase shift matrix, W is the coding matrix, B is the additive white Gaussian noise matrix, P is the number of RIS deployed in the subcluster, p = 1, ..., P, K is the number of transmission time blocks, k = 1, ..., K; D p (E) is the diagonal matrix operator of the RIS phase shift matrix, which means placing the pth row of the matrix E on the main diagonal of a diagonal matrix, D k (W) is the diagonal matrix operator of the encoding matrix, which means placing the kth row of the matrix W on the main diagonal of a diagonal matrix.

4. A multi-RIS-assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 3, characterized in that: The first cost function and the second cost function are constructed based on the cutting results, which are specifically expressed as follows: Where X is the symbol matrix, F is the coupling channel matrix, is the symbolic matrix estimate, is the estimated value of the coupling channel matrix, and Can be regarded as tensors The horizontal and side slices of , ⊙ is the Khatri Rao product, and i is the number of iterations.

5. A multi-RIS assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 1, characterized in that: The base station received signal is reconstructed based on the symbol matrix estimation value to obtain the reconstructed received signal, specifically: a pseudo-inverse operation is performed on the symbol matrix estimation value to obtain a generalized inverse matrix, an inverse operation is performed on the diagonal matrix operator of the coding matrix to obtain an inverse matrix, and the generalized inverse matrix and the inverse matrix of the diagonal matrix operator of the coding matrix are multiplied by the base station received signal to obtain the reconstructed received signal.

6. A multi-RIS-assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 1, characterized in that: The third cost function and the fourth cost function are constructed based on the cutting results, and are specifically expressed as follows: Among them, Q is the equivalent matrix of the RIS sub-cluster to user channel matrix, G is the user to RIS sub-cluster channel matrix, E is the RIS phase shift matrix, is the estimated value of the channel matrix, and Tensors The horizontal and side slices of , ⊙ is the Khatri Rao product, and i is the number of iterations.

7. A multi-RIS assisted semi-blind joint channel estimation and symbol detection method as claimed in claim 1, characterized in that: The multiple RISs are divided into several subclusters according to preset conditions, and the specific division satisfies the following relationship: Where P is the number of RIS deployed in a sub-cluster, and P≥2, j=2,…,P, x1 is the distance from the base station to the first RIS, x j is the distance from the base station to the jth RIS, y1 is the distance from the user to the first RIS, y j is the distance from the user to the jth RIS, d j is the distance from the first RIS to the jth RIS, θ j is the angle between the path from the base station to the first RIS and the path from the base station to the jth RIS, is the angle between the user’s path to the first RIS and the user’s path to the jth RIS, τ and λ are the deployment angle thresholds, ω1 and ω j are the angles between the first and j-th RIS planes and the X-axis of the reference coordinate system with the base station as the origin, α1 and α j are the angles between the first and j-th RIS planes and the Y axis of the reference coordinate system with the base station as the origin, β1 and β j are the angles between the first and j-th RIS planes and the Z axis of the reference coordinate system with the base station as the origin.

8. A multi-RIS-assisted semi-blind joint channel estimation and symbol detection system, characterized in that: include: A system model building unit, which is used to build a MIMO communication system model assisted by multiple RISs; wherein the multiple RISs are divided into a number of subclusters according to preset conditions; A symbol matrix estimation unit is configured to construct a noise-free received signal of a base station into a parallel factor tensor model based on the constructed multi-RIS-assisted MIMO communication system model; cut the parallel factor tensor model along horizontal and vertical slices through balanced factor decomposition, and construct a first cost function and a second cost function based on the cutting results; and obtain a symbol matrix estimate and a coupling channel matrix estimate by alternately optimizing the first cost function and the second cost function; A channel matrix estimation unit is configured to reconstruct a base station received signal based on the symbol matrix estimate to obtain a reconstructed received signal; construct the reconstructed received signal into a parallel factor tensor model, slice the parallel factor tensor model along horizontal and vertical planes through balanced factor decomposition, and construct a third cost function and a fourth cost function based on the slice results; and obtain an estimated value of the channel matrix by alternately optimizing the third and fourth cost functions, thereby realizing multi-RIS-assisted semi-blind joint channel estimation and symbol detection.

9. An electronic device comprising a memory, a processor, and a computer program stored and running on the memory, characterized in that: When the processor executes the program, the method for semi-blind joint channel estimation and symbol detection assisted by multiple RIS is implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the program implements the multi-RIS-assisted semi-blind joint channel estimation and symbol detection method according to any one of claims 1 to 7.

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