Clustering-based RIS auxiliary spatial modulation system blind detection method and system

Abstract

The invention discloses a clustering-based RIS auxiliary spatial modulation system blind detection method and system, and relates to the technical field of wireless communication, and the method comprises the steps: constructing an RIS auxiliary spatial modulation system, and obtaining a receiving signal of the RIS auxiliary spatial modulation system; reconstructing a received signal by constructing a matrix to obtain a channel model; performing equivalent transformation on the channel model to obtain an equivalent channel model; converting a signal detection problem into a clustering problem through an equivalent channel model; constructing an optimized K-means clustering detector based on the amplitude and phase characteristics; and iterating a clustering problem by optimizing a K-means clustering detector to realize blind detection of the signal. According to the method, the signal detection problem in the RIS auxiliary spatial modulation system is converted into the clustering problem, the amplitude and phase characteristics of the system channel are deeply excavated through the novel K-means clustering detector, the signal detection precision and efficiency are effectively improved, and dependence on channel state information is avoided.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more particularly to a clustering-based RIS-assisted spatial modulation system blind detection method and system. Background Art

[0002] In the field of wireless communications, reconfigurable smart surfaces (RIS) have received widespread attention in recent years as a potential candidate technology for 6G wireless networks. RIS can manipulate the propagation environment of RF signals to improve spectral efficiency (SE) and energy efficiency (EE). On the other hand, spatial modulation (SM) is a technology that goes beyond 5G and has high energy efficiency and spectral efficiency by using antenna indexes to transmit information in combination with traditional modulation schemes. Therefore, combining RIS with SM has become a promising direction for promoting the development of beyond 5G (B5G) networks.

[0003] There are three forms of RIS-assisted spatial modulation (RIS-SM) models, where spatial modulation can be located at the transmitter, RIS, and receiver. As research progresses, the RIS as part of the transmitter and the spatial modulation system model at the receiver (RIS-RSM) have gradually become the focus of research.

[0004] At present, the research on RIS-RSM is still in its preliminary stage, especially in the field of signal detection. In addition, existing research on signal detection in RIS-RSM systems usually assumes that the channel state information (CSI) is completely known. For example, related studies have proposed a suboptimal detector based on maximum energy (greedy detector, GD) and an optimal detector using exhaustive search (maximum likelihood detector, ML), but these methods all assume the existence of perfect CSI. However, due to the passive nature of RIS, pilot symbols cannot be transmitted and received, so obtaining accurate CSI faces significant challenges in actual deployment. Existing technologies rely on unsupervised clustering algorithms in machine learning to eliminate the need for CSI. However, traditional clustering algorithms, such as K-means, still have insufficient performance in the application of RIS-RSM systems.

[0005] Therefore, how to propose a clustering-based RIS-assisted spatial modulation system blind detection method and system, by deeply mining the amplitude and phase characteristics of the system channel, and by using the unsupervised clustering algorithm in machine learning to achieve effective signal detection under unknown CSI conditions is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0006] In view of this, the present invention provides a clustering-based RIS-assisted spatial modulation system blind detection method and system, which deeply mines the amplitude and phase characteristics of the system channel and realizes effective signal detection under unknown CSI conditions through the unsupervised clustering algorithm in machine learning. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A clustering-based RIS-assisted spatial modulation system blind detection method, comprising:

[0008] Constructing a RIS-assisted spatial modulation system and obtaining a receiving signal of the RIS-assisted spatial modulation system;

[0009] The channel model is obtained by reconstructing the received signal by constructing a matrix;

[0010] Performing equivalent transformation on the channel model to obtain an equivalent channel model;

[0011] The signal detection problem is converted into a clustering problem through an equivalent channel model;

[0012] Construct an optimized K-means clustering detector based on amplitude and phase characteristics;

[0013] The blind detection of signals is achieved by iteratively optimizing the K-means clustering detector.

[0014] Optionally, the RIS-assisted spatial modulation system includes: an intelligent reflecting surface RIS, a receiving antenna, a radio frequency source, an input bit stream and a segmentation module; the input bit stream is segmented by the segmentation module, and the second part of the segmented input bit stream is modulated with symbols and received by the radio frequency source and transmitted to the intelligent reflecting surface RIS, and the first part is used to select the receiving antenna index and send it to the intelligent reflecting surface RIS.

[0015] Optionally, the first part contains log2 N r bits, used to select the receiving antenna index i, and the smart reflector RIS adjusts the phase according to the selected receiving antenna to maximize the energy of the target antenna; the second part contains log2M bits, which are used to modulate the symbol s m , the symbol is transmitted from the RF source to the smart reflector RIS by phase shift keying or quadrature amplitude modulation.

[0016] Optionally, the intelligent reflection surface RIS is composed of N passive reflection elements, and the receiving end is configured with N r The channel matrix between the receiving antenna, the intelligent reflector RIS and the base station receiving antenna is expressed as G = {g l,i},in represents the wireless fading channel between the i-th passive reflector element and the l-th receiving antenna, β l,i With ψl,i Corresponding to g l,i amplitude and phase.

[0017] Optionally, also include:

[0018] Let all channel phases ψ l,i It is known that the smart reflector RIS adjusts the phase of the reflected signal to maximize the energy of the target receiving antenna u. The phase adjustment vector of the smart reflector RIS is expressed as Where T represents transpose, j represents the imaginary unit, and the element φ in the phase adjustment vector Ψ of the smart reflector RIS u,i =-arg(g u,i )=ψ u,i .

[0019] Optionally, acquiring a received signal of a RIS-assisted spatial modulation system includes:

[0020] Get the signal y received in the nth time slot n for:

[0021] y n =GΨs m +n n ;

[0022] Among them, n n is additive white Gaussian noise, following a complex Gaussian distribution N0 represents the noise power, Represents N r ×1-dimensional all-zero vector, Represents N r ×1-dimensional all-1 vector, s m is from the set S signal ={s1,…,s m ,…,s M}The modulation M-PSK / QAM symbol selected in}, where PSK is phase shift keying and QAM is orthogonal amplitude modulation.

[0023] Optionally, the amplitude and phase characteristics include:

[0024] Amplitude characteristics: When the receiving antenna corresponding to the activated transmitting antenna index receives a signal, the amplitude is larger than when other antennas are activated;

[0025] Phase characteristic: The phase difference between the received signal of the receiving antenna with the same index as the activated transmitting antenna and the transmitted PSK or QAM signal m Same phase.

[0026] Optionally, converting the signal detection problem into a clustering problem through an equivalent channel model includes:

[0027] Y=H eq X+N;

[0028] Where X = [x1, x2, …, x L ] is the transmission signal matrix, Y=[y1,y2,…,y L ] is the received signal matrix, N=[n1,n2,…,n L ] is the noise matrix, L represents the time slot length, the received signals of L time slots are regarded as L observation values, the signals received in the time slots with the same transmitted signal are classified into one class, and the blind detection of RIS-assisted spatial modulation system is transformed into a clustering problem, and the number of clusters is K = N r M, M is the modulation signal order.

[0029] Optionally, constructing an optimized K-means clustering detector based on amplitude and phase characteristics includes:

[0030] S1: According to Gaussian distribution Generate h i ' i ,h i ' i Denotes the equivalent channel H generated by this Gaussian distribution eq The diagonal elements h ii The true value of Get the optimized set of cluster centers where c′ k is an N r ×1 vector in the form of c′ k ={0,…,h i ' i s m ,…,0};

[0031] S2: Each received signal y n Assign to the nearest cluster In the calculation

[0032] S3: Update the cluster center according to the average value of all data points assigned to each cluster;

[0033] S4: Repeat S2 and S3 until the cluster center no longer moves or the maximum number of iterations is reached;

[0034] S5: Map the clustering results back to information bits, where each cluster corresponds to a specific receiving antenna index and modulation symbol, to achieve blind detection of the signal.

[0035] Optionally, a clustering-based RIS-assisted spatial modulation system blind detection system comprises:

[0036] Signal acquisition module: used to construct the RIS-assisted spatial modulation system and obtain the receiving signal of the RIS-assisted spatial modulation system;

[0037] Reconstruction module: used to reconstruct the received signal by constructing a matrix to obtain a channel model;

[0038] Equivalent transformation module: used to perform equivalent transformation on the channel model to obtain an equivalent channel model;

[0039] Conversion module: used to convert the signal detection problem into a clustering problem through an equivalent channel model;

[0040] Building module: used to build an optimized K-means clustering detector based on amplitude and phase characteristics;

[0041] Blind detection module: used to iteratively implement blind detection of signals by optimizing the K-means clustering detector.

[0042] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a clustering-based RIS-assisted spatial modulation system blind detection method and system, which has the following beneficial effects:

[0043] The present invention proposes a clustering-based RIS-assisted spatial modulation system blind detection method, comprising: constructing a RIS-assisted spatial modulation system and acquiring a received signal of the RIS-assisted spatial modulation system; reconstructing the received signal by constructing a matrix to obtain a channel model; performing an equivalent transformation on the channel model to obtain an equivalent channel model; converting a signal detection problem into a clustering problem by using the equivalent channel model; constructing an optimized K-means clustering detector based on amplitude and phase characteristics; and iterating the clustering problem by optimizing the K-means clustering detector to realize blind detection of the signal.

[0044] The clustering-based blind detection method of the present invention converts the signal detection problem in the RIS-assisted spatial modulation system into a clustering problem, avoiding the reliance on channel state information (CSI). This method realizes signal detection through an unsupervised clustering algorithm in machine learning, significantly simplifying the channel information acquisition process of the system; in view of the unique channel characteristics of the RIS-RSM system, a new K-means clustering detector is designed, which proposes an optimized K-means initialization scheme by deeply mining the amplitude and phase characteristics of the system channel, which effectively improves the accuracy and efficiency of signal detection, while overcoming the limitations of traditional clustering algorithms in such applications, especially in processing RIS-RSM system signal detection; this method can still maintain efficient detection performance close to that of the optimal detector (such as the maximum likelihood detector) without obtaining channel state information, bringing significant progress to the field of RIS-RSM signal detection, and is particularly suitable for scenes where perfect CSI cannot be obtained in practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0046] Figure 1 A schematic flow chart of a clustering-based RIS-assisted spatial modulation system blind detection method provided by the present invention.

[0047] Figure 2 A schematic diagram of a clustering-based RIS-assisted spatial modulation system blind detection system provided by the present invention.

[0048] Figure 3 This is a flow chart of the blind detection algorithm of the improved K-means clustering algorithm provided by the present invention.

[0049] Figure 4 This is a comparison chart of the BER performance of the detector provided by the present invention and the prior art. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The embodiment of the present invention discloses a clustering-based RIS-assisted spatial modulation system blind detection method, such as Figure 1 As shown, including:

[0052] Constructing a RIS-assisted spatial modulation system and obtaining a receiving signal of the RIS-assisted spatial modulation system;

[0053] The channel model is obtained by reconstructing the received signal by constructing a matrix;

[0054] Performing equivalent transformation on the channel model to obtain an equivalent channel model;

[0055] The signal detection problem is converted into a clustering problem through an equivalent channel model;

[0056] Construct an optimized K-means clustering detector based on amplitude and phase characteristics;

[0057] The blind detection of signals is achieved by iteratively optimizing the K-means clustering detector.

[0058] In a specific implementation, a clustering-based RIS-assisted spatial modulation system blind detection system includes:

[0059] Signal acquisition module: used to construct the RIS-assisted spatial modulation system and obtain the receiving signal of the RIS-assisted spatial modulation system;

[0060] Reconstruction module: used to reconstruct the received signal by constructing a matrix to obtain a channel model;

[0061] Equivalent transformation module: used to perform equivalent transformation on the channel model to obtain an equivalent channel model;

[0062] Conversion module: used to convert the signal detection problem into a clustering problem through an equivalent channel model;

[0063] Building module: used to build an optimized K-means clustering detector based on amplitude and phase characteristics;

[0064] Blind detection module: used to iteratively implement blind detection of signals by optimizing the K-means clustering detector.

[0065] Specific schematic diagram, such as Figure 2 As shown, the specific principles include:

[0066] Step 1: Create a system model

[0067] (1) Consider a RIS-assisted receiver spatial modulation (RIS-RSM) system, such as Figure 2 As shown in the figure, RIS is a component of the transmitter, which consists of N passive and low-cost reflective elements, and the receiver is equipped with Nr The channel matrix between the RIS and the base station receiving antenna is expressed as G = {g l,i},in represents the wireless fading channel between the i-th reflection unit and the l-th receiving antenna, β l,i With ψ l,i Corresponding to g l,i amplitude and phase.

[0068] (2) Assume that all channel phases ψ l,i It is known that RIS can adjust the phase of the reflected signal to maximize the energy of the target receiving antenna u. The phase adjustment vector of RIS is expressed as T represents transpose, and j represents the imaginary unit. The element φ in the phase adjustment vector Ψ of RIS u,i =-arg(g u,i )=ψ u,i .

[0069] (3) In the RIS-RSM system, the input bit stream is divided into two parts. The first part contains log2 N r bits, used to select the receive antenna index i, and the RIS adjusts its phase according to the selected receive antenna to maximize the energy of the target antenna. The second part contains log2 M bits, which are used to modulate the symbol s m , the symbols are transmitted from a radio frequency (RF) source to the RIS via phase shift keying (PSK) or quadrature amplitude modulation (QAM).

[0070] (4) The signal y received in the nth time slot n It can be expressed as:

[0071] y n =GΨs m +n n ;

[0072] Among them, n n is additive white Gaussian noise (AWGN), following a complex Gaussian distribution N0 represents the noise power, Represents N r ×1-dimensional all-zero vector, Represents N r ×1-dimensional all-1 vector, s m is from the set S signal ={s1,…,s m ,…,s M}The modulation M-PSK / QAM symbol selected in.

[0073] Step 2: Transform the problem

[0074] (1) Consider B to be an N×N r dimensional matrix, where each element b l,i The definition is as follows:

[0075]

[0076] in, Indicates g l,i The conjugation of.

[0077] (2) Then the signal y n Rewritten as:

[0078] y n =GBx n +n n ;

[0079] Among them, x n The signal energy transmitted in the nth time slot is expressed by the following formula:

[0080]

[0081] Among them, e i It is from the collection Select N r dimensional standard basis vectors, Is Each element in S signal ={s1,…,s m ,…,s M} is a set of combinations formed by pairing each element of M is the modulation signal order.

[0082] (3) The rewritten signal y n The channel model is converted into the following form:

[0083] y n =H eq x n +n n ;

[0084] Among them, H eq is called an equivalent channel and H eq =GB.

[0085] It can be seen that the RIS-RSM system is equivalent to the traditional SM system, the difference lies in the channel matrix.

[0086] (4) In the equivalent channel model, the signal y n The channel switching model is further expressed as:

[0087] Y=H eq X+N;

[0088] Among them, the transmission signal matrix X=[x1,x2,…,x L ], the received signal matrix Y = [y1,y2,…,y L ], the noise matrix N = [n1,n2,…,n L ], L represents the time slot length. The received signals of L time slots are regarded as L observation values, so the signals received in the same time slot as the transmitted signal can be divided into one class. Therefore, the blind detection of the RIS-RSM system can be transformed into a clustering problem, and the number of clusters is K = N. r M.

[0089] In a specific implementation, since the signal detection problem of RIS-RSM has been restated as a clustering task, the classic K-means clustering algorithm is introduced into the RIS-RSM system. However, the traditional K-means clustering algorithm randomly selects the initial clustering center, which leads to the floor effect problem. Although repeated running P times can alleviate the error platform problem, it also increases the complexity of the algorithm exponentially.

[0090] In order to avoid the limitations of traditional clustering algorithms, a novel K-means clustering detector is proposed. This detector effectively solves the local optimal problem by making full use of the amplitude and phase characteristics of the equivalent channel of the RIS-RSM system, while achieving excellent detection performance with low complexity.

[0091] (1) Amplitude and phase characteristics

[0092] From the above analysis, we can see that the received signal in the RIS-RSM system can be effectively divided into small clusters. According to the characteristics of the channel, the following conclusions can be drawn:

[0093] Amplitude characteristics: When the receiving antenna corresponding to the activated transmitting antenna index receives a signal, it exhibits higher energy (ie, larger amplitude) than when other antennas are activated.

[0094] Phase characteristic: The phase difference between the received signal of the receiving antenna with the same index as the activated transmitting antenna and the transmitted PSK or QAM signal m Same phase.

[0095] (2) Proposed detector

[0096] Based on the above amplitude and phase characteristics, an improved K-means clustering detector suitable for RIS-RSM system is designed. Figure 3 As shown, the specific steps are as follows:

[0097] Step 1: Determine the optimal cluster center (initialize the cluster center):

[0098] 1) According to Gaussian distribution Generate h i ' i ;h i ' i Denotes the equivalent channel H generated by this Gaussian distribution eq The diagonal elements h ii The true value of .

[0099] 2) Form a collection

[0100] 3) Obtain an optimized set of cluster centers where c′ k is an N r ×1 vector, the specific form is c′ k ={0,…,h i ' i s m ,…,0}.

[0101] Step 2: Signal Assignment:

[0102] Each received signal y n Assign to the nearest cluster In the calculation

[0103] Step 3: Cluster center update:

[0104] The cluster centers are updated based on the mean of all data points assigned to each cluster.

[0105] Step 4: Iterative optimization:

[0106] Repeat steps 2 and 3 until the cluster center no longer moves or the maximum number of iterations is reached.

[0107] Step 5: Map the clustering results:

[0108] The clustering results are mapped back to information bits, and each cluster corresponds to a specific receive antenna index and modulation symbol, thereby achieving blind detection of the signal.

[0109] In the specific implementation, in order to verify the performance of the proposed improved K-mean detector, it is compared with other existing technologies, including a maximum energy-based greedy detector that requires known CSI and an optimal maximum likelihood (ML) detector, and a series of experiments are conducted, such as Figure 4 shown.

[0110] The experiment was conducted in a simulated RIS-assisted spatial modulation system environment. Figure 4 The system parameters are as follows: the number of RIS elements N is 64, the number of receiving antennas Nr is 4, Figure 4The signal modulation modes in (a) and (b) are QPSK and 8QAM respectively. Experimental results show that compared with the traditional K-means (KMC), the proposed improved K-means (KMC) detector effectively avoids the floor effect and only requires one initialization instead of multiple initializations, which greatly reduces the computational complexity. In the case of unknown CSI, it achieves detection performance comparable to the optimal ML detector with known CSI.

[0111] The present invention transforms the blind detection problem into a clustering problem and utilizes the characteristics of the RIS-RSM system channel to propose a novel K-means clustering detector. This detector can achieve detection performance similar to that of the optimal ML detector without the need for CSI, providing important technical support for the development of 5G and future communication networks.

[0112] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0113] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A clustering-based RIS-assisted spatial modulation system blind detection method, characterized in that: include: Constructing a RIS-assisted spatial modulation system and obtaining a receiving signal of the RIS-assisted spatial modulation system; The channel model is obtained by reconstructing the received signal by constructing a matrix; Performing equivalent transformation on the channel model to obtain an equivalent channel model; The signal detection problem is converted into a clustering problem through an equivalent channel model; Construct an optimized K-means clustering detector based on amplitude and phase characteristics; The blind detection of signals is achieved by iteratively optimizing the K-means clustering detector.

2. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 1, characterized in that: The RIS-assisted spatial modulation system comprises: an intelligent reflecting surface RIS, a receiving antenna, a radio frequency source, an input bit stream and a segmentation module; the input bit stream is segmented by the segmentation module, the second part of the segmented input bit stream is modulated with symbols and received by the radio frequency source and transmitted to the intelligent reflecting surface RIS, and the first part is used to select a receiving antenna index and sent to the intelligent reflecting surface RIS.

3. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 2, characterized in that: The first part contains log2 N r bits, used to select the receiving antenna index i, and the smart reflector RIS adjusts the phase according to the selected receiving antenna to maximize the energy of the target antenna; the second part contains log2M bits, which are used to modulate the symbol s m , the symbol is transmitted from the RF source to the smart reflector RIS by phase shift keying or quadrature amplitude modulation.

4. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 2, characterized in that: The intelligent reflection surface RIS is composed of N passive reflection elements, and the receiving end is configured with N r The channel matrix between the receiving antenna, the intelligent reflector RIS and the base station receiving antenna is expressed as G = {g l,i },in represents the wireless fading channel between the i-th passive reflector element and the l-th receiving antenna, β l,i With ψ l,i Corresponding to g l,i amplitude and phase.

5. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 2, characterized in that: Also includes: Let all channel phases ψ l,i It is known that the smart reflector RIS adjusts the phase of the reflected signal to maximize the energy of the target receiving antenna u. The phase adjustment vector of the smart reflector RIS is expressed as Where T represents transpose, j represents the imaginary unit, and the element φ in the phase adjustment vector Ψ of the smart reflector RIS u,i =-arg(g u,i )=ψ u,i .

6. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 1, characterized in that: The obtaining of a received signal of the RIS-assisted spatial modulation system comprises: Get the signal y received in the nth time slot n for: the n =GΨs m +n n ; Among them, n n is additive white Gaussian noise, following a complex Gaussian distribution N0 represents the noise power, Represents N r ×1-dimensional all-zero vector, Represents N r ×1-dimensional all-1 vector, s m is from the set S signal ={s1,…,s m ,…,s M }The modulation M-PSK / QAM symbol selected in}, where PSK is phase shift keying and QAM is orthogonal amplitude modulation.

7. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 1, characterized in that: The amplitude and phase characteristics include: Amplitude characteristics: When the receiving antenna corresponding to the activated transmitting antenna index receives a signal, the amplitude is larger than when other antennas are activated; Phase characteristic: The phase difference between the received signal of the receiving antenna with the same index as the activated transmitting antenna and the transmitted PSK or QAM signal m Same phase.

8. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 1, characterized in that: The converting of the signal detection problem into a clustering problem through an equivalent channel model includes: Y=H eq X+N; Where X = [x1, x2, …, x L ] is the transmission signal matrix, Y=[y1,y2,…,y L ] is the received signal matrix, N=[n1,n2,…,n L ] is the noise matrix, L represents the time slot length, the received signals of L time slots are regarded as L observation values, the signals received in the time slots with the same transmitted signal are classified into one class, and the blind detection of RIS-assisted spatial modulation system is transformed into a clustering problem, and the number of clusters is K = N r M, M is the modulation signal order.

9. The clustering-based RIS-assisted spatial modulation system blind detection method according to claim 1, characterized in that: The method of constructing an optimized K-means clustering detector based on amplitude and phase characteristics includes: S1: According to Gaussian distribution Generate h i ' i ,h i ' i Denotes the equivalent channel H generated by this Gaussian distribution eq The diagonal elements h ii The true value of Get the optimized set of cluster centers where c′ k is an N r ×1 vector in the form of c′ k ={0,…,h i ' i s m ,…,0}; S2: Each received signal y n Assign to the nearest cluster In the calculation S3: Update the cluster center according to the average value of all data points assigned to each cluster; S4: Repeat S2 and S3 until the cluster center no longer moves or the maximum number of iterations is reached; S5: Map the clustering results back to information bits, where each cluster corresponds to a specific receiving antenna index and modulation symbol, to achieve blind detection of the signal.

10. A clustering-based RIS-assisted spatial modulation system blind detection system, characterized in that: include: Signal acquisition module: used to construct the RIS-assisted spatial modulation system and obtain the receiving signal of the RIS-assisted spatial modulation system; Reconstruction module: used to reconstruct the received signal by constructing a matrix to obtain a channel model; Equivalent transformation module: used to perform equivalent transformation on the channel model to obtain an equivalent channel model; Conversion module: used to convert the signal detection problem into a clustering problem through an equivalent channel model; Building module: used to build an optimized K-means clustering detector based on amplitude and phase characteristics; Blind detection module: used to iteratively implement blind detection of signals by optimizing the K-means clustering detector.

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