A method for multi-user and rate maximization of sensing and communication dual-ris-isac in the presence of line-of-sight interruption

Abstract

The embodiment of the application provides a line-of-sight interruption-oriented sensing and communication dual-RIS-ISAC multi-user and sum rate maximization method. The method is applied to the technical field of wireless communication, and for a multi-user ISAC system with blocked sensing line-of-sight link, a dual-RIS-assisted ISAC system model is constructed; based on the dual-RIS-assisted ISAC system model, a joint optimization problem of active beamforming matrix and passive beamforming matrix is constructed under the sensing signal-to-interference-and-noise ratio constraint and the communication signal-to-interference-and-noise ratio constraint with the multi-user sum rate maximization as the target; the semi-definite relaxation method is used to convert the sensing signal-to-interference-and-noise ratio constraint and the communication signal-to-interference-and-noise ratio constraint into convex constraints, and the successive convex approximation method is used to convert the joint optimization problem into a convex optimization problem; the convex optimization problem is decoupled into a base station transmit beamforming optimization sub-problem, a communication RIS phase shift matrix optimization sub-problem and a sensing RIS phase shift matrix optimization sub-problem, and the alternating optimization method is used to iteratively obtain the optimal solution of the three sub-problems, thereby improving the sum rate of multiple communication users.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method for maximizing multi-user and rate maximization in a sensing dual RIS-ISAC for line-of-sight interruption. Background Technology

[0002] The booming development of the low-altitude economy has spurred diversified demands for wireless services. From drone logistics and low-altitude traffic management to urban emergency communications and intelligent inspection, these scenarios place high demands on both the reliability of information transmission and the accuracy of environmental perception in wireless systems. The traditional model of independently deploying wireless and radar sensing systems with separate spectrum not only leads to low spectrum resource utilization but also suffers from severe inter-device interference and high deployment costs, making it difficult to adapt to the dynamic and ever-changing service needs in low-altitude environments. The emergence of Integrated Sensing and Communications (ISAC) technology provides a key solution to this dilemma. By integrating communication and sensing functions onto the same platform, ISAC systems can simultaneously complete information transmission and target detection tasks, achieving efficient reuse of spectrum resources, reducing system deployment costs, and minimizing inter-device interference, thus becoming a key technology supporting 6G applications.

[0003] In the design and optimization research of ISAC systems, hybrid sensing beamforming optimization is crucial. Typically, the focus is on improving sensing accuracy while meeting communication rate requirements, or maximizing communication performance while maintaining sensing performance thresholds. Most current research schemes assume the existence of a line-of-sight (LoS) link between the base station and the communication user / sensing target. However, in core low-altitude economic application scenarios and complex urban environments, ISAC systems often face obstruction scenarios such as urban canyons, dense building clusters, indoor spaces, or rugged terrain, making the LoS link highly susceptible to interruption or severe attenuation. This not only significantly reduces the reliability, stability, and rate of communication transmission but also compromises the accuracy of sensing and target recognition, making it difficult for ISAC systems to meet the performance requirements of practical applications. Reconfigurable Intelligent Surface (RIS), as one of the key technologies of 6G, offers an effective way to solve the aforementioned ISAC challenges due to its unique physical characteristics and flexible control capabilities. RIS (Reflection Array) consists of numerous low-cost, low-power passive reflective units. It can flexibly control the amplitude, phase, and polarization of incident electromagnetic waves through software programming, thereby passively guiding the electromagnetic waves to a preferred direction in space. By deploying RIS between the base station and the communication user / sensing target, a non-line-of-sight (NLoS) sensing link can be established, achieving both expanded system coverage and improved link stability while maintaining deployment flexibility and cost control. Due to its unique advantages, RIS technology has become an ideal enhancement method for ISAC (Inductively Coupled Array) systems in complex scenarios. Furthermore, existing literature on RIS-assisted ISAC systems largely focuses on scenarios involving single RIS-assisted ISAC systems.

[0004] In summary, the existing technology suffers from a lack of consideration for dual-RIS-assisted ISAC systems, focusing instead on single-RIS-assisted enhancement of synesthesia performance. For more complex scenarios where both synesthesia and line-of-sight are blocked, there is an urgent need for an architecture for a dual-RIS-assisted ISAC system, as well as a method for maximizing multi-user and rate performance in dual-RIS-ISAC systems with line-of-sight interruption. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method for maximizing the sum of data and rate in a dual RIS-ISAC system for line-of-sight interruptions, comprising:

[0006] For multi-user ISAC systems with obstructed line-of-sight communication links, a dual-RIS-assisted ISAC system model is constructed. The dual-RIS-assisted ISAC system model includes: a dual-function base station with communication and sensing capabilities, multiple single-antenna communication users, a single-antenna sensing target, a communication RIS, and a sensing RIS.

[0007] Based on the dual RIS-assisted ISAC system model, with the goal of maximizing multi-user and rate, a joint optimization problem of active beamforming matrix and passive beamforming matrix is ​​constructed under the constraints of sensing signal-to-interference-plus-noise ratio and communication signal-to-interference-plus-noise ratio.

[0008] The semidefinite relaxation method is used to transform the sensing signal-to-interference-plus-noise ratio (SINR) constraint and the communication signal-to-interference-plus-noise ratio (SINR) constraint into convex constraints, and the successive convex approximation method is used to transform the joint optimization problem into a convex optimization problem.

[0009] The convex optimization problem is decoupled into a base station transmit beamforming optimization subproblem, a communication RIS phase shift matrix optimization subproblem, and a sensing RIS phase shift matrix optimization subproblem;

[0010] The alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem until the preset convergence conditions are met, and the optimal base station transmit beamforming matrix, the optimal communication RIS phase shift matrix, and the optimal sensing RIS phase shift matrix are output.

[0011] Optionally, the joint optimization problem satisfies the following formula:

[0012]

[0013] Where K represents the number of users, M represents the number of antennas, and N represents the number of RIS units. Indicates a communication beamformer. Indicates a sensing beamformer. Represents the synesthetic RIS phase shift matrix. Indicates user Signal-to-noise ratio at the location This represents the perceived signal-to-interference-plus-noise ratio (SIR). This represents the minimum threshold for the perceived signal-to-interference-plus-noise ratio (SINR). This represents the minimum threshold for the signal-to-interference-plus-noise ratio (SIR) of each communication user. Represents the communication beamforming vector. Represents the sensing beamforming vector. This represents the base station's maximum transmit power budget. Representing the first RIS Phase shift of each reflecting unit Indicates communication RIS, Represents the perception of RIS.

[0014] Optionally, a semi-definite relaxation method is used to convert the sensing signal-to-interference-plus-noise ratio (SIR) constraint and the communication SIR constraint into convex constraints, and a successive convex approximation method is used to convert the joint optimization problem into a convex optimization problem, including:

[0015] Define a positive semidefinite matrix , and auxiliary variables , for The k-th variable transforms the optimization problem into:

[0016]

[0017] in, For communication beamforming matrix, To sense the beamforming matrix, , The channel matrix from the base station to the communication RIS, for The conjugate transpose of . , The standard deviation of additive white Gaussian noise for communication. The channel matrix from the base station to the sensing target. for The conjugate transpose of . This refers to the number of antennas at the base station. The variance of the perceived Gaussian white noise;

[0018] In the In each iteration, the variable value at the current iteration solution is used as the expansion point. A first-order Taylor expansion is performed on the non-convex constraint to obtain a linear approximation constraint. The original non-convex constraint is replaced by the linear approximation constraint, thereby transforming the joint optimization problem into a convex approximation problem.

[0019] Optionally, the alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem, including:

[0020] The phase shift matrices of the fixed communication RIS and the sensing RIS are used to obtain the transformed optimization problem, and the solution to the transformed optimization problem is Gaussian randomized.

[0021] The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. User weight factors are introduced to balance the communication quality of different users. By using vectorization and quadratic reconstruction, the phase shift of each unit of the communication RIS is obtained.

[0022] The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. The rank-1 approximation method is used to simplify the sensing signal power, and the simplified sensing signal power is substituted into the transformed optimization problem for solution to obtain the phase shift of each unit of the sensing RIS.

[0023] Optionally, by fixing the phase shift matrix of the communication RIS and the phase shift matrix of the sensing RIS, the transformed optimization problem is obtained, which satisfies the following formula:

[0024]

[0025] in, .

[0026] Optionally, the communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem, which satisfies the following formula:

[0027]

[0028] in, For communication RIS phase shift matrix, The first RIS indicates communication Phase shift of each reflective unit.

[0029] Optionally, the communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem, which satisfies the following formula:

[0030]

[0031] in, To sense the phase shift vector of RIS, For the first time to perceive RIS Phase shift of each reflecting unit;

[0032] The rank-1 approximation method is used to simplify the sensing signal power, and the simplified sensing signal power is substituted into the transformed optimization problem, satisfying the following formula:

[0033]

[0034] in, For the equivalent channel vector, This represents the channel matrix from the base station to the sensing RIS. To perceive the target response of RIS, To be The transformed rank-1 matrix.

[0035] The beneficial effects of this invention are:

[0036] Compared with existing technologies, this invention considers scenarios where both communication and sensing LoS links are blocked, and utilizes dual RIS to assist the ISAC system, thus overcoming the deficiency of a single RIS in this scenario, which cannot simultaneously handle communication and sensing links. By jointly designing the base station active beamforming matrix and the RIS passive phase shift matrix, and utilizing the AO algorithm that integrates SDR and SCA, the sum rate of multiple communication users is significantly improved.

[0037] To address the multivariable coupled nonconvex optimization problem, this invention first uses the SDR method to transform the nonconvex constraints into convex ones, and then uses the SCA method to linearize the nonlinear objective function. The resulting active-passive and communication-sensing hybrid beamforming joint design problem is decoupled into three sub-problems. Then, an alternating optimization method is used to iteratively find the optimal solution for these three sub-problems, improving both the solution efficiency and the quality of the solution. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 A flowchart illustrating a method for maximizing the rate of a syn-sensory dual RIS-ISAC multi-user system oriented towards line-of-sight interruption, provided in an embodiment of this application;

[0040] Figure 2 This is a schematic diagram of a dual RIS-assisted ISAC system model provided in an embodiment of this application;

[0041] Figure 3 This application provides a comparison chart of communication user and rate performance as the base station transmit power budget changes.

[0042] Figure 4 The graph shows the comparison results of communication user and rate performance as the number of RIS units changes, as provided in the embodiments of this application.

[0043] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0046] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0047] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart illustrating a method for maximizing the rate of a syn-sensory dual RIS-ISAC multi-user system oriented towards line-of-sight interruptions, provided in an embodiment of this application. Figure 1 As shown, this embodiment provides Figure 1 A flowchart illustrating a multi-user and rate maximization method for syn-sensory dual RIS-ISAC with line-of-sight interruption provided in this application embodiment includes:

[0049] S1: For multi-user ISAC systems with obstructed line-of-sight communication links, construct a dual RIS-assisted ISAC system model.

[0050] Figure 2 This is a schematic diagram of a dual-RIS-assisted ISAC system model provided in an embodiment of this application. The constructed dual-RIS-assisted ISAC system model includes a dual-function sensing base station, One single-antenna communication user and one single-antenna sensing target. The base station is equipped with... A uniform linear array of antennas, with an antenna spacing of [value missing]. Simultaneously transmitting communication signals and radar waveforms. In complex environments, due to obstacle obstruction, assuming that the line-of-sight links between the target and the user and the base station are both blocked and spatially separated, it is impossible to provide simultaneous services to both using a single RIS. Therefore, two dedicated RISs are deployed to assist in communication and sensing respectively. Each RIS is a passive RIS and contains... There are 3 reflective units, denoted as a uniform planar array, with the number of units per row and per column denoted as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... and Its phase shift vector is defined as ,in Indicates the first Phase shift of each unit, subscript , Used to represent the communication RIS. Used to represent Sensing RIS;

[0051] Time slot The discrete-time baseband radar waveform at that point is defined as ; will be sent to The discrete-time complex baseband downlink communication symbol for a single user is defined as follows: Define the sensing beamformer as Define the communication beamformer as Define the base station in the time slot The emitted complex baseband signal is ; This represents the channel matrix from the base station to the communication RIS. Indicates the communication from RIS to the user The channel vector.

[0052] Therefore, the equivalent concatenated channel between the base station and the communication user is represented as: ,in, .user The received signal at the location is: ,in, User Additive white Gaussian noise at the location.

[0053] Therefore, users The signal-to-interference-plus-noise ratio at this location is:

[0054]

[0055] definition This represents the channel matrix from the base station to the sensing RIS; This represents the channel vector from the sensing RIS to the target; Represents the target response of the perception RIS, where It is the amplitude of the complex target; and These are the azimuth and elevation angles of the target relative to the sensing RIS, respectively. This represents the RIS steering vector, and , ,in, For wavelength, This represents the number of units in each row of the RIS. This represents the number of cells in each column of the RIS.

[0056] Therefore, the echo signal received by the base station is: ,in, ; It is additive white Gaussian noise at the base station. Let V be the variance of the white noise. It is an M-order identity matrix.

[0057] Therefore, the perceived signal-to-interference-plus-noise ratio can be expressed as:

[0058] .

[0059] S2: Based on the dual RIS-assisted ISAC system model, with the goal of maximizing multi-user and rate, it constructs a joint optimization problem of active beamforming matrix and passive beamforming matrix under the constraints of sensing signal-to-interference-plus-noise ratio and communication signal-to-interference-plus-noise ratio.

[0060] Specifically, the joint optimization problem satisfies the following formula:

[0061] (1)

[0062] in, Indicates a communication beamformer. Indicates a sensing beamformer. Represents the synesthetic RIS phase shift matrix. Indicates user Signal-to-noise ratio at the location This represents the perceived signal-to-interference-plus-noise ratio (SIR). This represents the minimum threshold for the perceived signal-to-interference-plus-noise ratio (SINR). This represents the minimum threshold for the signal-to-interference-plus-noise ratio (SIR) of each communication user. Represents the communication beamforming vector. Represents the sensing beamforming vector. This represents the base station's maximum transmit power budget. Representing the first RIS Phase shift of each reflecting unit Indicates communication RIS, Represents the perception of RIS. This indicates that the perceived signal-to-interference-plus-noise ratio should be greater than its minimum threshold. , This means that the signal-to-interference-plus-noise ratio (SIR) of each communication user should be greater than its minimum threshold , This indicates the base station's transmit power constraint. This indicates a constraint on the RIS reflection coefficient.

[0063] S3: The semidefinite relaxation method is used to convert the sensing signal-to-interference-plus-noise ratio (SINR) constraint and the communication signal-to-interference-plus-noise ratio (SINR) constraint into convex constraints, and the successive convex approximation method is used to convert the joint optimization problem into a convex optimization problem.

[0064] Specifically, it includes the following steps:

[0065] S31. Define the matrix , Inductive beamforming vector and Transform into a positive semi-definite matrix, where and Each satisfies , , , Meanwhile, the beamforming matrix for multi-user communication is defined as follows: The sensing beamforming matrix is .

[0066] S32. For communication non-convex constraints Define a positive semidefinite matrix , The channel matrix from the base station to the communication RIS, for The conjugate transpose of can be transformed into:

[0067] (2)

[0068] in, The standard deviation of additive white Gaussian noise for communication.

[0069] S33. For perceptual nonconvex constraints Based on the mathematical equivalence between trace operation and norm It can be restated as This can be further expressed as:

[0070] (3)

[0071] in, The channel matrix from the base station to the sensing target. for The conjugate transpose of . This refers to the number of antennas at the base station. The variance of the perceived Gaussian white noise is given.

[0072] S34. Order Define a set of auxiliary variables , the problem Convert to:

[0073] (4)

[0074] Constraints Further processing yields Define functions at the same time Assuming in the first... The solution obtained in the next iteration is At that point, the function Performing a first-order Taylor expansion yields its linear approximation. Therefore, constraints In the In the next iteration, it is approximated as:

[0075] (5)

[0076] By performing the above steps, the non-convex constraint was successfully converted into a convex constraint, and the nonlinear objective function was converted into a linear function.

[0077] S4: Decouple the convex optimization problem into a base station transmit beamforming optimization subproblem, a communication RIS phase shift matrix optimization subproblem, and a sensing RIS phase shift matrix optimization subproblem.

[0078] S5: The alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem until the preset convergence condition is met, and the optimal base station transmit beamforming matrix, the optimal communication RIS phase shift matrix, and the optimal sensing RIS phase shift matrix are output.

[0079] The preset convergence conditions can be, for example, reaching the maximum number of iterations or the difference between two iterations being less than a preset value.

[0080] Specifically, the alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem, including the following steps:

[0081] S51: The phase shift matrix of the fixed communication RIS and the phase shift matrix of the sensing RIS are used to obtain the transformed optimization problem, and the solution to the transformed optimization problem is Gaussian randomized.

[0082] Specifically, the fixed communication RIS phase shift matrix and the perception RIS phase shift matrix Optimize the synesthetic beamforming matrix to address the problem. Convert to:

[0083] (6)

[0084] Due to the problem The solution obtained , Since it is high-rank, Gaussian randomization is used to restore it to a rank-1 solution. Eigenvalue decomposition yields ,in It is a matrix whose column vectors are eigenvectors; It is a diagonal matrix, where the diagonal elements are the corresponding non-negative eigenvalues. It is also randomly generated. A single complex Gaussian random vector ,in It is a K-order identity matrix.

[0085] Based on the eigenvalue decomposition results and the complex Gaussian random vector, the candidate communication beamforming vector is constructed as follows:

[0086] (7)

[0087] in, Let be the square root matrix of the eigenvalues. Similarly, candidate sensing beamforming vectors can be constructed.

[0088] (8)

[0089] Due to the constraint of transmission power, the candidate vectors need to be normalized. First, the total transmission power of the candidate vectors is calculated as follows:

[0090]

[0091] like Then the candidate vector will be scaled to , ;like Then let , .Will and Substitution problem Calculate the corresponding sum rate And verify whether it satisfies and Constraints, if not satisfied and If there is a constraint, discard the value. From all candidate vectors that satisfy the conditions, select the one that allows... The largest set of candidate vectors is taken as the final solution and obtained and .

[0092] S52: The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. User weight factors are introduced to balance the communication quality of different users. Using vectorization and quadratic reconstruction, the phase shift of each unit of the communication RIS is obtained.

[0093] Specifically, the communication beamforming matrix of a fixed base station and sensing beamforming matrix Optimization problem This can be equivalent to:

[0094] (9)

[0095] in, For communication RIS phase shift matrix, The first RIS of communication Phase shift of each reflective unit.

[0096] Set up users Weighting factors Define function Define vector ,in, For the conjugate of the channel matrix from communication RIS to user k, then user The desired signal power can be reformulated as Substitute it into the function achievable ,in, The problem is to assign a weight factor to each communication user. This can be further equivalent to:

[0097] (10)

[0098] in, It is a positive semi-definite matrix. Finally, the communication RIS first... The optimal phase shift for each unit is:

[0099] (11)

[0100] S53: The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. The rank-1 approximation method is used to simplify the sensing signal power, and the simplified sensing signal power is substituted into the transformed optimization problem for solution to obtain the phase shift of each unit of the sensing RIS.

[0101] Specifically, the communication beamforming matrix of a fixed base station and sensing beamforming matrix Optimization problem This can be equivalent to:

[0102] (12)

[0103] in, To sense the phase shift vector of RIS, For the first time to perceive RIS Phase shift of each reflective unit.

[0104] Due to variables In equation (12), the numerator and denominator of the objective function are highly coupled and difficult to decompose directly. Therefore, the rank-1 approximation method is used to further solve it. Approximately a rank-1 matrix

[0105] (13)

[0106] in, for The largest eigenvalue, for The corresponding feature vector, This is the approximate rank-1 matrix. for The transpose and conjugate matrix of . Therefore, the sensing signal power can be simplified to:

[0107] (14)

[0108] For brevity, the perceived target response is abbreviated as... , To recover the target amplitude, To perceive the target response of RIS, for The transpose and conjugate of the first element, then combined with... Substituting into equation (14), we get:

[0109] (15)

[0110] Ignore constant terms The perceived RIS phase shift variable in equation (15) was discovered. Simultaneously coupled with the forward link (base station to sensing RIS) gain and backward link (sensing RIS to sensing target) gain Therefore, it is quite difficult to find a closed-form solution directly from the original problem. It was found that the backward link exhibits a smooth overall change, while the forward link gain... The phase alignment directly determines whether RIS can be coherently superimposed. Therefore, in this embodiment, equation (15) is approximated as follows: .question It can be approximated as:

[0111] (16)

[0112] in, Represents the equivalent channel vector. Solving the problem... When the objective function is obtained exist Each element and The maximum value is obtained when the corresponding elements are out of phase:

[0113] (17)

[0114] If and only if When the equality holds, therefore the perception RIS is valid. The optimal phase shift for each unit is:

[0115] (18)

[0116] In an optional embodiment, the present invention compares the performance of two benchmark schemes under different parameter settings.

[0117] Baseline Scheme 1 (Random RIS Scheme): The base station beamforming matrix optimization scheme is consistent with that in this invention, and the inductive RIS phase shift matrix is ​​randomly generated.

[0118] Baseline Solution 2 (Greedy Algorithm): By fixing other optimization variables, the optimal solution for each optimization variable is searched one cell / vector at a time.

[0119] Figure 3 This is a graph showing the comparison of communication user and rate performance as the base station transmit power budget changes, provided as an embodiment of this application. Figure 3 As shown, with the gradual increase in base station transmit power budget, the number of communication users and the data rate increase for all schemes. This is because higher transmit power provides the system with higher signal transmission power, effectively improving the signal-to-interference-plus-noise ratio at the user receiver, all other things being equal, thus directly improving system performance. Observing the two curves of the same color representing the same scheme, we can see the number of antennas. It will also affect the performance of the ISAC system, for example, Its performance is better than This is because as the number of antennas increases, the base station can provide better beamforming performance.

[0120] Figure 4 This is a graph showing the comparison of communication user and rate performance as the number of RIS (Resonance Components) units changes, provided in an embodiment of this application. Figure 4 As shown, with the increase in the number of synesthetic RIS units With the increase of the number of RIS units, the multi-user communication speed of both the algorithm proposed in this invention and the greedy algorithm show an upward trend. This is because increasing the number of RIS units increases the efficiency of communication. This can bring higher passive reflection gain to the system, enhance the received signal power of the user, effectively increase the received signal power at the communication user end, and further improve the signal-to-interference-plus-noise ratio of the communication user, thereby improving the communication user's signal-to-interference-plus-noise ratio. However, the performance under the randomized RIS scheme remains almost unchanged. This is because the random phase of the RIS makes it impossible to achieve controllable coherent superposition and directional reflection of the incident signal. This disrupts the coherence between signals, making it difficult to focus the reflected signal onto the communication user and achieve effective beam gain and power enhancement.

[0121] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0122] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for maximizing the sum of data and rate in a synesthetic dual RIS-ISAC system oriented towards line-of-sight interruption, characterized in that: The method includes: For multi-user ISAC systems with obstructed line-of-sight communication links, a dual-RIS-assisted ISAC system model is constructed. The dual-RIS-assisted ISAC system model includes: a dual-function base station with communication and sensing capabilities, multiple single-antenna communication users, a single-antenna sensing target, a communication RIS, and a sensing RIS. Based on the dual RIS-assisted ISAC system model, with the goal of maximizing multi-user and rate, a joint optimization problem of active beamforming matrix and passive beamforming matrix is ​​constructed under the constraints of sensing signal-to-interference-plus-noise ratio and communication signal-to-interference-plus-noise ratio. The semidefinite relaxation method is used to transform the sensing signal-to-interference-plus-noise ratio (SINR) constraint and the communication signal-to-interference-plus-noise ratio (SINR) constraint into convex constraints, and the successive convex approximation method is used to transform the joint optimization problem into a convex optimization problem. The convex optimization problem is decoupled into a base station transmit beamforming optimization subproblem, a communication RIS phase shift matrix optimization subproblem, and a sensing RIS phase shift matrix optimization subproblem; The alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem until the preset convergence conditions are met, and the optimal base station transmit beamforming matrix, the optimal communication RIS phase shift matrix, and the optimal sensing RIS phase shift matrix are output.

2. The method according to claim 1, characterized in that, The joint optimization problem satisfies the following formula: Where K represents the number of users, M represents the number of antennas, and N represents the number of RIS units. Indicates a communication beamformer. Indicates a sensing beamformer. Represents the synesthetic RIS phase shift matrix. Indicates user Signal-to-noise ratio at the location This represents the perceived signal-to-interference-plus-noise ratio (SIR). This represents the minimum threshold for the perceived signal-to-interference-plus-noise ratio (SINR). This represents the minimum threshold for the signal-to-interference-plus-noise ratio (SIR) of each communication user. Represents the communication beamforming vector. Represents the sensing beamforming vector. This represents the base station's maximum transmit power budget. Representing the first RIS Phase shift of each reflecting unit Indicates communication RIS, Represents the perception of RIS.

3. The method according to claim 2, characterized in that, The semi-definite relaxation method is used to transform the sensing signal-to-interference-plus-noise ratio (SIR) and communication SIR constraints into convex constraints. The successive convex approximation method is then used to transform the joint optimization problem into a convex optimization problem, including: Define a positive semidefinite matrix , and auxiliary variables , for The k-th variable transforms the optimization problem into: in, For communication beamforming matrix, To sense the beamforming matrix, , The channel matrix from the base station to the communication RIS, for The conjugate transpose of . , The standard deviation of additive white Gaussian noise for communication. The channel matrix from the base station to the sensing target. for The conjugate transpose of . This refers to the number of antennas at the base station. The variance of the perceived Gaussian white noise; In the In each iteration, the variable value at the current iteration solution is used as the expansion point. A first-order Taylor expansion is performed on the non-convex constraint to obtain a linear approximation constraint. The original non-convex constraint is replaced by the linear approximation constraint, thereby transforming the joint optimization problem into a convex approximation problem.

4. The method according to claim 3, characterized in that, The alternating optimization method is used to iteratively solve the base station transmit beamforming optimization subproblem, the communication RIS phase shift matrix optimization subproblem, and the sensing RIS phase shift matrix optimization subproblem, including: The phase shift matrices of the fixed communication RIS and the sensing RIS are used to obtain the transformed optimization problem, and the solution to the transformed optimization problem is Gaussian randomized. The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. User weight factors are introduced to balance the communication quality of different users. By using vectorization and quadratic reconstruction, the phase shift of each unit of the communication RIS is obtained. The communication beamforming matrix and sensing beamforming matrix of the fixed base station are used to obtain the transformed optimization problem. The rank-1 approximation method is used to simplify the sensing signal power, and the simplified sensing signal power is substituted into the transformed optimization problem for solution to obtain the phase shift of each unit of the sensing RIS.

5. The method according to claim 4, characterized in that, The phase shift matrices of the fixed communication RIS and the sensing RIS are used to obtain the transformed optimization problem, which satisfies the following formula: in, .

6. The method according to claim 4, characterized in that, The communication beamforming matrix and sensing beamforming matrix of a fixed base station are used to obtain the transformed optimization problem, which satisfies the following formula: in, For communication RIS phase shift matrix, The first RIS indicates communication Phase shift of each reflective unit.

7. The method according to claim 4, characterized in that, The communication beamforming matrix and sensing beamforming matrix of a fixed base station are used to obtain the transformed optimization problem, which satisfies the following formula: in, To sense the phase shift vector of RIS, For the first time to perceive RIS Phase shift of each reflecting unit; The rank-1 approximation method is used to simplify the sensing signal power, and the simplified sensing signal power is substituted into the transformed optimization problem, satisfying the following formula: in, For the equivalent channel vector, This represents the channel matrix from the base station to the sensing RIS. To perceive the target response of RIS, To be The transformed rank-1 matrix.

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