Anti-interference method based on reconfigurable intelligent surface auxiliary inductance integrated system

Abstract

The invention discloses an anti-interference method based on a reconfigurable intelligent surface auxiliary inductance integrated system, and the method comprises the steps: building a reconfigurable intelligent surface auxiliary inductance integrated system model, and proposes an optimization problem of joint design of transmitted beam forming, a receiving filter and reconfigurable intelligent surface phase shift. An iterative algorithm combining optimization minimization and successive convex approximation technology is provided, a receiving filter is optimized by adopting a minimum variance undistorted response solution in a closed form, and a problem is converted into a series of convex subproblems easy to process. According to the system model, a communication function and a sensing function are integrated on a hardware platform, advantage complementation and resource sharing of the communication function and the sensing function are achieved, accurate positioning and tracking of a sensing target can be achieved through assistance of the intelligent reflecting surface, meanwhile, normal operation of a communication system is not affected, the intelligent reflecting surface is composed of a plurality of reflecting units, and the intelligent reflecting surface is more flexible. And the number and layout of the modules can be adjusted according to actual needs so as to adapt to different application scenes and demands.

Description

Technical Field

[0001] The invention relates to the technical field of communication technology, and in particular to an anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integrated system. Background Art

[0002] In recent years, as an innovative technology, reconfigurable smart surface (RIS) has brought a breakthrough solution to the field of wireless communications; this technology can not only overcome the problem of blocked communication links, but also significantly improve system performance. Smart reflective surface is actually a special planar device, which consists of a central controller and a large number of low-cost electronic reflective elements. Under the control of the controller, these elements can flexibly adjust the signal phase without complex RF processing. Due to its simple design, the smart reflective surface can be easily installed on the surface of the building, and the overall performance of the wireless communication system can be comprehensively improved by synergistically optimizing the direct and reflected signal paths.

[0003] The ISAC system is a cutting-edge technology in the current wireless communication field. It innovatively integrates wireless communication and radar perception functions. This integrated system completes both communication and perception tasks by sharing hardware platforms, spectrum resources and signal processing units. It can not only transmit data information, but also detect and locate target objects. Compared with traditional independent systems, ISAC not only greatly improves the efficiency of spectrum utilization, but also reduces equipment costs and power consumption. This integrated design can be widely used in multiple fields such as intelligent transportation, unmanned driving, smart cities, etc., and provides users with a richer and more accurate service experience by processing communication and perception data at the same time.

[0004] A secure communication method and device assisted by an intelligent reflector disclosed in a Chinese patent document with a publication number of CN112073102B, wherein the secure communication method and device assisted by the intelligent reflector establish a SWIPT Internet of Things system model assisted by an intelligent reflector; jointly optimize the base station transmit beamforming matrix, the jammer covariance matrix and the phase shift of the intelligent reflector to construct an optimization problem of maximizing energy collection; utilize relaxation variables, semidefinite relaxation methods, auxiliary variables and sequence parameter convex approximation methods to transform the non-convex quadratic problem into an equivalent convex problem, and propose an alternating iterative optimization algorithm to obtain a feasible solution to the original problem. The present invention combines the intelligent reflector and the jammer to establish a secure communication link of a SWIPT Internet of Things system assisted by an intelligent reflector, jointly optimizes the base station transmit beamforming matrix, the jammer covariance matrix and the phase shift of the intelligent reflector, improves communication security, and enhances the energy collection capability of the system, but the secure communication method and device assisted by the intelligent reflector have poor effects on the combination of communication and perception, and it is difficult to take into account the reliability of communication and the accuracy of perception, resulting in poor spectrum utilization and system performance.

[0005] A method for designing secure transmission with integrated synaesthesia assisted by an airborne intelligent reflective surface is disclosed in a Chinese patent document with publication number CN116669035A. The method assumes that an aerial target is a potential eavesdropper, introduces artificial noise, and establishes a system model of secure transmission with integrated synaesthesia assisted by an airborne intelligent reflective surface. Secondly, under the condition of meeting the signal-to-noise ratio requirement of target perception, the deployment position of the UAV, the active beamforming of the base station, the passive beamforming of the intelligent reflective surface and the power of the artificial noise are jointly optimized to construct an optimization problem of maximizing system security and rate. Finally, the original non-convex problem is decomposed into three sub-problems, and the original non-convex problem is converted into a convex problem by using continuous convex approximation and semidefinite relaxation, and an alternating iterative algorithm is developed for iterative solution. The present invention proposes a design method for jointly optimizing the deployment position of UAVs, base station transmit beamforming, intelligent reflector phase shift matrix and artificial noise power, aiming to maximize the sum of the system's safe communication rates under the signal-to-noise ratio requirements of target perception. However, the airborne intelligent reflector-assisted synaesthesia integrated safe transmission design method is mainly used in the field of UAV deployment and is not universal for conventional communication technologies.

[0006] The existing integrated synaesthesia system needs to realize communication and perception functions simultaneously on the same spectrum, which has potential interference problems and affects the reliability and stability of communication.

[0007] In order to solve the above-mentioned deficiencies in the prior art, it is a problem worth studying to provide an anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integrated system. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the existing synaesthesia integration system that needs to realize communication and perception functions on the same spectrum at the same time, has potential interference problems, and affects the reliability and stability of communication. An anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integration system is provided to achieve anti-interference, efficient and stable synaesthesia integration signal transmission.

[0009] The purpose of the present invention is achieved through the following technical solutions:

[0010] An anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integrated system comprises the following steps:

[0011] S1: Establish a reconfigurable intelligent surface-assisted synaesthesia integrated system model;

[0012] S2: An optimization problem for jointly designing transmit beamforming, receive filter, and phase shift of a reconfigurable smart surface is proposed;

[0013] S3: An iterative algorithm combining optimal minimization and successive convex approximation techniques is proposed to transform the problem into several tractable sub-problems and solve them iteratively.

[0014] The reconfigurable intelligent surface-assisted synaesthesia integrated system model in step S1 includes a multi-antenna base station, a single-antenna interference base station, a single-antenna user, and a L The multi-antenna base station can utilize antenna array gain and beamforming technology to improve the reliability and rate of signal transmission. The intelligent reflecting surface can optimize the signal transmission path and further enhance the signal reception strength by adjusting the phase and amplitude of its reflecting unit. The existence of the single-antenna interfering base station simulates the interference source in the real environment, so that the system can take into account the impact of interference in the design and testing phase and take corresponding anti-interference measures.

[0015] The step S2 specifically includes:

[0016] The channel between systems is modeled as a Rice channel. Under the constraints of user transmit power, IRS reflection phase and transmission time slot, the maximum throughput of the system is expressed as follows:

[0017] The optimization problem is established by maximizing the weighted sum of the achievable communication rate and the radar signal-to-interference-noise ratio. This problem is achieved by jointly designing three key system parameters: the receive filter u, the transmit beamforming vector w, and the RIS phase shift Θ. The optimization problem is:

[0018]

[0019] The Rice channel model takes into account the existence of a dominant, stable signal path accompanied by multiple weaker, randomly changing signal paths. This characteristic enables the dominant signal path to provide a more stable and reliable communication link in the RIS-assisted synaesthesia integration system.

[0020] The step S3 specifically includes:

[0021] An iterative algorithm combining optimized minimization (MM) and successive convex approximation (SCA) techniques is proposed. The receive filter is optimized using the closed-form minimum variance distortion-free response solution, and the transmit beamforming is solved by semidefinite programming relaxation. The reconfigurable smart surface phase shift optimization uses optimized minimization and successive convex approximation methods to transform the problem into a series of tractable convex subproblems. The combination of optimized minimization and successive convex approximation techniques enables the system to more accurately optimize the receive filter and transmit beamforming, thereby improving the communication rate and the accuracy of radar perception.

[0022] Positive beneficial effects:

[0023] 1. The anti-interference method based on the reconfigurable intelligent surface-assisted interaceptive integrated system, the system model can be easily expanded to more complex network scenarios, such as multi-user, multi-base station, multi-sensing targets, etc., to provide a flexible infrastructure for future 6G communication and perception systems. The intelligent reflective surface is mainly composed of passive components and does not require additional power supply, so it has the characteristics of low power consumption. By optimizing the signal transmission path and suppressing interference signals, it can improve energy utilization efficiency and reduce unnecessary energy consumption, which is in line with the current global demand for sustainable development and energy conservation and emission reduction.

[0024] 2. The anti-interference method based on the reconfigurable intelligent surface-assisted synaesthesia integrated system can form beamforming for a specific direction, thereby suppressing interference signals and improving the system's anti-interference ability, which helps to achieve higher communication rates and more accurate perception results under limited spectrum resources.

[0025] 3. The anti-interference method based on the reconfigurable intelligent surface-assisted synaesthesia integrated system and the study of this optimization problem can promote technological innovation and development in related fields, such as exploring more efficient optimization algorithms, more advanced RIS designs, and more flexible communication and perception strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of a method flow of an ISAC of a RIS provided by an embodiment of the present invention;

[0027] Figure 2 This is a model diagram of the system for implementing the present invention;

[0028] Figure 3 is a graph showing the relationship between transmit power and system performance provided by the implementation of the present invention;

[0029] Figure 4 is a graph showing the relationship between the transmission power of an interfering base station and system performance provided by the implementation of the present invention;

[0030] Figure 5 is a graph showing the relationship between the number of RIS reflection units and the communication rate provided by an embodiment of the present invention;

[0031] Figure 6 4 is a diagram showing the relationship between the number of RIS reflection units and radar SINR provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0033] like Figures 1 to 6 As shown, an anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integrated system includes the following steps.

[0034] S1: Establish a system model of inter-sensory base station, interference base station, RIS, single antenna user, and sensing target;

[0035] S2: Jointly optimize the transmit beamforming, receive filter and RIS phase shift to obtain the optimization problem of maximizing the weighted sum of the achievable communication rate and radar signal-to-interference-noise ratio.

[0036] S3: An iterative algorithm combining optimal minimization (MM) and successive convex approximation (SCA) techniques is proposed. The receive filter is optimized using a closed-form minimum variance distortion-free response solution, the transmit beamforming is solved by semidefinite programming relaxation, and the RIS phase shift optimization uses MM and SCA methods to transform the problem into a series of tractable convex subproblems.

[0037] Specifically, step S1 includes:

[0038] The method described in this embodiment is applied to Figure 2 The system model shown in the figure includes: an ISAC base station equipped with multiple antennas, an interference base station with a single antenna, a user with a single antenna, and a RIS. The transmission power of the ISAC base station is 40 dBm, the power of the interference base station is 20 dBm, the number of reflection units of the RIS is 40, and the wireless channel adopts a Rice channel.

[0039] Through a multi-antenna base station, a single-antenna interfering base station, a single-antenna user, and a LThe reconfigurable intelligent surface-assisted interaceptive integrated system model consists of an intelligent reflective surface with a reflective unit and a sensing target. The multi-antenna base station can use antenna array gain and beamforming technology to improve the reliability and rate of signal transmission. The intelligent reflective surface can optimize the signal transmission path by adjusting the phase and amplitude of its reflective unit, and further enhance the signal reception strength. The existence of a single-antenna interference base station simulates the interference source in the real environment, so that the system can consider the impact of interference in the design and testing stage and take corresponding anti-interference measures. Through the reflection and regulation of the intelligent reflective surface, the interference signal can be suppressed and eliminated, and the anti-interference performance of the system can be improved. In addition, the system model integrates communication and perception functions on a hardware platform, achieving the advantages of both. Potential complementarity and resource sharing, the assistance of the intelligent reflective surface can achieve accurate positioning and tracking of the perceived target without affecting the normal operation of the communication system. The intelligent reflective surface is composed of multiple reflective units, and its number and layout can be adjusted according to actual needs to adapt to different application scenarios and needs. The system model can be easily expanded to more complex network scenarios, such as multi-user, multi-base station, multi-perception target, etc., to provide a flexible infrastructure for future 6G communication and perception systems. The intelligent reflective surface is mainly composed of passive components and does not require additional power supply. Therefore, it has the characteristics of low power consumption. By optimizing the signal transmission path and suppressing interference signals, it can improve energy utilization efficiency and reduce unnecessary energy consumption, which is in line with the current global demand for sustainable development and energy conservation and emission reduction.

[0040] Further, step S2 includes:

[0041] The BS transmits communication signals to the user while performing sensing operations on the target. To enhance the communication and sensing capabilities, the RIS with programmable electromagnetic elements is deployed and managed by a dedicated controller that can dynamically adjust the phase shift to optimize the propagation environment. Various unauthorized or uncooperative wireless devices may act as interference sources, potentially destroying the communication reliability and sensing accuracy of the ISAC system. In our system model, such interference is modeled as a jammer. For the ISAC BS, the BS's transmission signal can be expressed as

[0042] x=Ws.

[0043] where w is the transmit beamforming vector and s is the ISAC symbol. Therefore, from a communication perspective, the received signal from the user is

[0044]

[0045] in and It represents the communication channels between BS and user, between RIS and user, and between BS and RIS. d，cis the interference channel between the jammer and the user, P represents the interference channel between the jammer and RIS. J is the transmission power of the jammer, q is the transmission signal of the jammer, where The reflection matrix is ​​defined as Θ = diag(v), where is the vector of reflection coefficients, satisfying |ν l |=1,l∈[1,N L ]. Based on the user's received signal, the communication signal to interference plus noise ratio (SINR) can be given as:

[0046]

[0047] In terms of sensing operation, signal transmission follows a two-stage process. Initially, the transmit signal propagates to the target via the RI reflection path, and then the target reflects back to the ICS BS. The echo signal received at the BS receive array can be mathematically formulated as:

[0048]

[0049] Where α0 represents the radar cross section (RCS) of the target, represents the interference channel between the jammer and the base station, represents the interference channel between the jammer and RIS, n s represents the receiving noise at the base station. In order to obtain satisfactory target detection performance, a receiving filter / beamformer is used After being processed by the linear finite impulse response receiving filter, the filtered receiving signal y r It can be expressed as:

[0050]

[0051] Therefore, the radar SINR for sensing is formulated as:

[0052]

[0053] This method proposes to evaluate the system performance by maximizing the weighted sum of the achievable communication rate and the radar signal-to-interference-noise ratio. This goal is achieved by jointly designing three key system parameters: the receive filter u, the transmit beamforming vector w, and the RIS phase shift Θ. The mathematical formula of the optimization problem can be expressed as:

[0054]

[0055] Where P c Transmit power budget, unit modulus requirement of RIS reflection coefficient.

[0056] By applying the Rice channel to the model construction of the reconfigurable intelligent surface-assisted synaesthesia integration system, the Rice channel model takes into account the existence of a dominant, stable signal path accompanied by multiple weaker, randomly changing signal paths. This characteristic makes the dominant signal path in the RIS-assisted synaesthesia integration system provide a more stable and reliable communication link. Due to the existence of the dominant signal path in the Rice channel, the received signal strength has a higher predictability, which helps to more accurately estimate and optimize the signal transmission performance in the synaesthesia integration system.

[0057] Under the Rice channel model, RIS can optimize the superposition effect of the dominant signal path and multiple weaker signal paths by intelligently adjusting the phase and amplitude of its reflection unit, thereby improving the communication quality. Due to the stability of the dominant signal path in the Rice channel, the RIS-assisted synaesthesia integrated system can maintain more stable communication performance and enhance the robustness of the system when facing environmental changes or interference. In complex indoor or outdoor environments, such as city streets, stadiums or office buildings, the Rice channel model can more accurately describe the signal transmission characteristics. RIS can optimize the signal propagation path through intelligent reflection and focusing, reduce blind spots, and enhance signal strength and stability. The Rice channel model provides a more accurate channel description for the RIS-assisted synaesthesia integrated system, which helps to achieve the coordinated optimization of sensing and communication functions. By intelligently adjusting the reflection characteristics of RIS, the system can simultaneously achieve efficient communication and accurate sensing functions;

[0058] Secondly, by maximizing the weighted sum of the achievable communication rate and the radar signal-to-interference-noise ratio to establish an optimization problem, the system can intelligently adjust the reflection characteristics of the RIS to maximize the weighted sum of the communication rate and the radar SINR. This helps to improve the perception performance of the radar while maintaining the communication quality. In a complex communication environment, interference signals may come from multiple directions. By optimizing the reflection characteristics of the RIS, the system can form beamforming for a specific direction, thereby suppressing interference signals and improving the system's anti-interference ability, which helps to achieve higher communication rates and more accurate perception results under limited spectrum resources. Studying this optimization problem can promote technological innovation and development in related fields, such as exploring more efficient optimization algorithms, more advanced RIS designs, and more flexible communication and perception strategies.

[0059] Further, step S3 comprises: Obviously, the problem is very difficult to solve due to the complex objective function with log(·), fractional terms and coupled variables in terms of both communication rate and radar SINR. In the following section, it is proposed to use optimization minimization (MM) and continuous convex approximation (SCA) methods to transform the problem into several tractable sub-problems and solve them iteratively.

[0060] To simplify the subsequent derivation, define Then, by fixing the transmit beamforming vector and the RIS phase shift, the optimization problem for the receive filter u can be formulated as:

[0061]

[0062] Convert it to equivalent

[0063]

[0064] The optimal receiver filter u can be derived in closed form as

[0065]

[0066] Continue to optimize the transmit beamforming vector w. To facilitate the statement of the problem, define Then, for a fixed RIS phase shift, the optimization problem regarding the transmit beamforming vector w can be formulated as:

[0067]

[0068] By definition And using semidefinite programming techniques, the problem can be equivalently reformulated as

[0069]

[0070] By removing the non-convex rank-one constraint, the above problem can be converted into a convex optimization problem. The relaxed problem becomes a convex optimization problem with respect to W, which can be solved efficiently using existing convex optimization algorithms.

[0071] After obtaining the optimal receive filter u and transmit beamforming vector w, we continue to optimize the RIS phase shift. In order to transform the problem into a more manageable form, we use algebraic relationships and rearrange the formula to

[0072]

[0073] in E=GWG H , f1=P J diag(f r，c )f j，c f d，c , F1=P J (diag(f r，c )f j，c )(diag(f r，c )f j，c ) H , and At the same time, define fj =u H f d，s , f jj =u H Hdiag(f r，s ), and Using the same mathematical transformation, the radar SINR can be summarized as

[0074]

[0075] The problem can be stated as

[0076]

[0077] In the problem, the optimization variable v exhibits different levels of complexity in the objective function components. Specifically, it exhibits a quadratic form in the communication rate term and a more challenging quartic form in the radar SINR expression. Therefore, the definition according to The radar SINR formula can be organized as

[0078]

[0079] x H G1x is a quartic term in v, which leads to an intractable objective. So we use the MM method again to find a favorable surrogate function. In particular, through the second-order Taylor expansion, at the point x s The appropriate upper limit of

[0080]

[0081] in is the largest eigenvalue of the matrix G1. Considering the amplitude constraint, it is clear that the term x H x is

[0082]

[0083] Therefore, we can get

[0084]

[0085] in G2 is the matrix form of g2, that is is non-convex, equivalently converting complex-valued functions into real-valued form

[0086]

[0087] Then, a second-order Taylor expansion is applied

[0088]

[0089] in is the Hessian matrix The maximum eigenvalue of therefore,

[0090]

[0091] The objective function for optimizing v can be transformed into

[0092]

[0093] in Next, define and

[0094]

[0095] The problem can be rewritten as

[0096]

[0097] To solve this problem, SCA technology is used to express it as

[0098]

[0099] in

[0100]

[0101] At this point, the problem becomes convex and can be solved efficiently using the CVX toolbox.

[0102] By combining optimization minimization and successive convex approximation technology, the system can more accurately optimize the receiving filter and transmitting beamforming, thereby improving the communication rate and the accuracy of radar perception, and transforming the non-convex reconfigurable smart surface phase shift optimization problem into a series of convex sub-problems, making the optimization process more efficient and able to find a solution close to the optimal solution, further improving system performance, reducing the complexity of the optimization problem, and improving optimization efficiency;

[0103] The minimum variance distortion-free response optimization method can minimize the impact of background noise or interference while maintaining the quality of the target signal, thereby enhancing the system's anti-interference ability and optimizing the phase shift of the reconfigurable smart surface. The system can form beamforming for a specific direction and effectively suppress interference signals from other directions. The algorithm can make full use of the reflection characteristics of the reconfigurable smart surface, realize intelligent regulation of communication and perception signals by optimizing the phase shift, and improve the utilization efficiency of spectrum resources.

[0104] The algorithm takes into account a variety of interference factors and processes them through an optimization process, so that the system can maintain relatively stable performance when facing environmental changes or interference. The minimum variance distortion-free response optimization method is combined with the reconfigurable intelligent surface phase shift optimization to enhance the system's ability to suppress noise and interference and improve the system's robustness. The algorithm can be further extended to complex scenarios such as multiple users and multiple antennas, and more complex performance optimization and anti-interference strategies can be achieved by introducing more optimization variables and constraints.

[0105] The system configuration parameters for this implementation are as follows:

[0106] Number of users K 1 Number of IRS reflection units N 40 <![CDATA[Base station transmit power P0]]> 40dBm <![CDATA[Noise σ 2 > -100dBm Interference base station power 20dBm Weight factor ρ 0.5

[0107] like Figure 3 Figure 2 shows the relationship between transmit power and the target value, which is defined as the sum of the communication rate and the radar SINR. As the transmit power increases from 25dBm to 45dBm, the target value shows an increasing trend for all the considered schemes. This observation highlights the positive impact of increased transmit power on the overall system performance, resulting in higher communication rates and improved radar SINR.

[0108] like Figure 4 Figure 2 shows the effect of interference power on the target value. Interference power represents the strength of adversarial interference and ranges from 0dBm to 20dBm. As the interference power increases, the target value of the scheme decreases. This deterioration in performance can be attributed to the harmful effects of the interference signal, which introduces additional interference and disrupts the communication and sensing processes. This downward trend highlights that the system is vulnerable to interference attacks and effective countermeasures are needed.

[0109] like Figure 5 Figure 2 shows the number of RIS reflective elements, as well as the power level, that affect the communication rate across different algorithms. As the number of elements increases from 10 to 50, the communication rate also increases.

[0110] like Figure 6 Figure 2 shows how adding RIS reflective elements improves radar SNR for different algorithms and power levels. With more RIS elements, the system can more effectively focus the transmitted signal to the desired target, exploit more independent propagation paths, achieve higher SNR, suppress unwanted signals or interference, and obtain more accurate CSI.

Claims

1. An anti-interference method based on a reconfigurable intelligent surface-assisted synaesthesia integrated system, characterized in that: The following steps are involved: S1: Establish a reconfigurable intelligent surface-assisted synaesthesia integrated system model; S2: An optimization problem for jointly designing transmit beamforming, receive filter, and phase shift of a reconfigurable smart surface is proposed; S3: An iterative algorithm combining optimal minimization and successive convex approximation techniques is proposed to transform the problem into several tractable sub-problems and solve them iteratively.

2. The anti-interference method based on the reconfigurable intelligent surface-assisted synaesthesia integrated system according to claim 1 is characterized by: The reconfigurable intelligent surface-assisted synaesthesia integrated system model in step S1 includes a multi-antenna base station, a single-antenna interference base station, a single-antenna user, and a L An intelligent reflective surface with 1 reflective unit and 1 sensing target.

3. The anti-interference method based on the reconfigurable intelligent surface-assisted synaesthesia integrated system according to claim 1 is characterized in that: The step S2 specifically includes: The channel between systems is modeled as a Rice channel. Under the constraints of user transmit power, IRS reflection phase and transmission time slot, the maximum throughput of the system is expressed as follows: The optimization problem is established by maximizing the weighted sum of the achievable communication rate and the radar signal-to-interference-noise ratio. This problem is achieved by jointly designing three key system parameters: the receive filter u, the transmit beamforming vector w, and the reconfigurable smart surface phase shift Θ. The optimization problem is: s.t.||w|| 2 <P c , |v l |=1,l∈[1,N L ],。 4. The anti-interference method based on the reconfigurable intelligent surface-assisted synaesthesia integrated system according to claim 1 is characterized in that: The step S3 specifically includes: An iterative algorithm combining optimized minimization (MM) and successive convex approximation (SCA) techniques is proposed. The receive filter is optimized using the closed-form minimum variance distortion-free response solution, and the transmit beamforming is solved by semidefinite programming relaxation. The phase shift optimization of the reconfigurable smart surface uses optimized minimization and successive convex approximation methods to transform the problem into a series of tractable convex subproblems.

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