A ris-assisted communication sensing integrated system, method, device and medium

Abstract

The application provides an RIS-assisted communication and sensing integrated system, method, device and medium. The system comprises an ISAC base station, a reconfigurable intelligent surface, a plurality of communication users adopting non-orthogonal multiple access, and a sensing target. The method comprises: constructing an optimization problem with the goal of maximizing the weighted sum of communication and sensing performance; introducing auxiliary variables to linearize the objective function; using alternating optimization to decompose the problem into auxiliary variable optimization, base station beamforming optimization and reconfigurable intelligent surface phase shift optimization subproblems; wherein the base station beamforming optimization and the reconfigurable intelligent surface phase shift optimization both use a continuous convex approximation algorithm based on a penalty term to handle non-convex rank-one constraints, and perform discrete mapping on the phase shift. The application solves the urban environment shielding problem through the reconfigurable intelligent surface, improves the spectrum efficiency through the non-orthogonal multiple access, realizes the flexible trade-off of the communication and sensing performance, and significantly improves the overall system performance and engineering practicability.

Description

Technical Field

[0001] This application relates to the fields of sixth-generation mobile communication technology (6G) and post-fifth-generation mobile communication technology (B5G), and more particularly to a RIS-assisted integrated communication and sensing system, method, device and medium. Background Technology

[0002] With the continuous development of emerging application scenarios such as intelligent transportation and the Industrial Internet of Things, future wireless networks face multiple challenges, including massive device connections, high spectrum efficiency, and high-precision environmental perception. Integrated Communication and Sensing (ISAC) technology, by sharing spectrum and hardware resources, can simultaneously perform communication and radar sensing functions, demonstrating great potential in significantly improving spectrum efficiency and reducing hardware costs, and is considered one of the core technologies of 6G networks.

[0003] However, in real-world urban deployment scenarios, the ISAC system faces severe challenges: on the one hand, the obstruction of tall buildings can interrupt the direct line-of-sight link between the base station and the remote user or sensing target, leading to a decline in communication quality and a deterioration in sensing performance; on the other hand, there is an inherent resource competition relationship between communication functions and sensing functions, and how to achieve coordinated optimization of their performance under limited power and spatial freedom is a problem that urgently needs to be solved.

[0004] Reconfigurable Smart Surfaces (RIS), a novel technology composed of numerous low-cost passive reflective elements, can dynamically reconstruct the wireless propagation environment by intelligently controlling the phase of reflected electromagnetic waves. Introducing RIS into ISAC systems can construct virtual line-of-sight links in obstructed environments, thereby enhancing the reliability of communication links and improving the target echo quality of sensed signals. Simultaneously, Non-Orthogonal Multiple Access (NOMA) technology, through power domain multiplexing and Sequential Interference Cancellation (SIC) mechanisms, allows multiple users to share the same time-frequency resources, providing new degrees of freedom for mitigating multi-user interference in ISAC systems, improving spectral efficiency, and ensuring sensing performance when spatial degrees of freedom are limited.

[0005] While some progress has been made in the research on the pairwise integration of RIS-ISAC and NOMA-ISAC, research on the deep integration of RIS, NOMA, and ISAC is still in its early stages. Existing research mostly focuses on specific scenarios for single performance optimization, and lacks a scheme for joint optimization of the weighted sum of communication-sensing performance in this integrated system. Summary of the Invention

[0006] This application aims to overcome the problem of communication building obstruction in urban environments and meet the requirements of adaptability to urban scenarios and flexible trade-off of sensing performance in multiple scenarios. It provides a reconfigurable intelligent surface-assisted non-orthogonal multiple access communication sensing integrated system, resource optimization allocation method, electronic device, storage medium and program product to solve the link reliability problem under urban obstruction and the problem of flexible trade-off of sensing performance in multiple scenarios, and ultimately improve the practicality and adaptability of the system in urban environments.

[0007] To achieve the above objectives, one aspect of this application proposes a RIS-assisted integrated communication and sensing system, comprising: An ISAC base station has dual functions of communication signal transmission and radar target perception, and is equipped with a uniform linear array; A reconfigurable smart surface (RIS), consisting of multiple passive reflective units, is deployed between a base station and a communication user to modulate the phase of reflected electromagnetic waves in order to construct a virtual line-of-sight link between the base station and the communication user. K communication users employ a non-orthogonal multiple access (NOMA) protocol and demodulate received signals using a serial interference cancellation (SIC) mechanism. Their decoding order is determined by the reconfigurable smart surface-assisted equivalent channel gain sequencing. L sensing targets; The base station transmits a composite signal of communication and sensing signals; the communication signal uses non-orthogonal multiple access multiplexing in the power domain; the sensing signal is orthogonal to the communication signal; and the reflective unit of the reconfigurable smart surface supports discrete phase modulation.

[0008] In some embodiments, the optimization objective of the system is to maximize the weighted sum of communication performance and sensing performance, wherein the communication performance is characterized by the normalized average communication rate, and the sensing performance is characterized by the normalized average sensing mutual information; the optimization variables include the communication beamforming matrix of the base station, the sensing beamforming matrix, and the phase shift matrix of the reconfigurable smart surface; the constraints include: the maximum transmit power constraint of the base station, the minimum signal-to-interference-plus-noise ratio constraint for each communication user, the minimum signal-to-interference-plus-noise ratio constraint for each sensing target, and the discrete unit mode constraint of the reconfigurable smart surface.

[0009] To achieve the above objectives, another aspect of this application proposes a resource optimization allocation method for the aforementioned system, the method comprising the following steps: Step S1: Construct the original optimization problem with the goal of maximizing the weighted sum of communication performance and sensing performance, and with constraints such as the maximum transmit power of the base station, the minimum signal-to-interference-plus-noise ratio of each communication user, the minimum signal-to-interference-plus-noise ratio of each sensing target, and the discrete phase of the reconfigurable smart surface. Step S2: Introduce auxiliary variables to equivalently transform the nonlinear objective function in the original optimization problem into a linear form, thus obtaining the transformed optimization problem; Step S3: Using an alternating optimization method, the transformed optimization problem is decomposed into an auxiliary variable optimization sub-problem, a base station beamforming optimization sub-problem, and a reconfigurable smart surface phase shift optimization sub-problem; Step S4: Iteratively solve the auxiliary variable optimization sub-problem, the base station beamforming optimization sub-problem, and the reconfigurable smart surface phase shift optimization sub-problem until the objective function value converges, and obtain the optimal resource allocation result.

[0010] In some embodiments, in step S2, based on the maximum point lemma of concave functions, auxiliary variables related to the communication signal-to-interference-plus-noise ratio (SINR) and the sensing signal-to-interference-plus-noise ratio (SINR) are introduced, and the logarithmic summation form in the original optimization problem is equivalently transformed into a combination of linear functions and auxiliary variables.

[0011] In some embodiments, the base station beamforming optimization sub-problem includes: fixing the auxiliary variable and the reconfigurable smart surface phase shift matrix, and jointly optimizing the communication beamforming matrix and the sensing beamforming matrix of the base station; Specifically, non-convex communication signal-to-interference-plus-noise ratio (SIR) constraints and sensing SIR constraints are transformed into convex constraints through linear matrix inequalities; and a continuous convex approximation algorithm based on penalty terms is used to process the rank-one non-convex constraints of the beamforming matrix, transforming the subproblem into a quadratic positive definite programming problem to be solved.

[0012] In some embodiments, the reconfigurable smart surface phase shift optimization sub-problem includes: fixing the auxiliary variables, the communication beamforming matrix, and the sensing beamforming matrix, and optimizing the phase shift matrix of the reconfigurable smart surface; Specifically, the non-convex phase shift constraint is transformed into an equivalent form through variable substitution; and a continuous convex approximation algorithm based on penalty terms is used to process the rank-one non-convex constraint of the substituted variables, transforming the subproblem into a quadratic positive semidefinite relaxation problem for solution.

[0013] In some embodiments, in the reconfigurable smart surface phase shift optimization subproblem, after obtaining the optimal continuous phase shift matrix, the optimal continuous phase shift matrix is ​​also mapped to a discrete phase set through the nearest neighbor mapping method to obtain a discretized reconfigurable smart surface phase shift matrix.

[0014] In some embodiments, the original optimization problem constructed in step S1 also considers imperfect channel state information, which is modeled based on minimum mean square error estimation.

[0015] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0018] Compared with the prior art, this application has the following advantages: 1) System Architecture Innovation: This application proposes a RIS-assisted NOMA-ISAC system. By deploying RIS to construct a virtual line-of-sight link, it completely solves the problem of base station-user direct link interruption caused by urban building obstruction. Through NOMA's power domain multiplexing and serial interference cancellation technologies, it supports massive concurrent user access. Simultaneously, relying on the sensing resource sharing characteristics of ISAC, it avoids hardware and spectrum redundancy. This architecture provides an integrated solution for 6G urban scenarios, filling the gap in existing research on performance optimization of multi-technology fusion systems.

[0019] 2) Flexible trade-off of sensing performance: This application constructs a NOMA-ISAC system assisted by RIS and uses the weighted sum of communication and sensing performance as the optimization target to flexibly adapt to various task requirements in urban occlusion scenarios (such as prioritizing communication during peak hours and prioritizing sensing during off-peak hours in intelligent transportation), which significantly improves the adaptability of the system and breaks through the limitation of "single-objective optimization" of existing ISAC systems.

[0020] 3) Superior Optimization Algorithm: For RIS phase shift, this application considers discrete phase modulation, which is closer to engineering applications. Based on this, a Continuous Convex Approximation (SCA) algorithm based on a penalty term is used to avoid transforming the non-convex phase shift constraint into a rank loss problem in solving a semidefinite programming problem. Simulation results show that the proposed algorithm converges quickly under both weight configurations, and the objective function value tends to stabilize after about 10 iterations. Its performance reaches over 97% of the theoretical upper limit and comprehensively outperforms benchmark schemes based on SDR, without RIS, or without NOMA. Attached Figure Description

[0021] Figure 1 This is a flowchart of a resource optimization allocation method for a RIS-assisted integrated communication and sensing system provided in an embodiment of this application; Figure 2 The graph shows the iterative convergence curves of the algorithm proposed in this application when the synesthesia tradeoff coefficient takes different values.

[0022] Figure 3 The graph shows the comparison of the synesthetic weighted performance of the proposed scheme and various benchmark schemes under different synesthetic trade-off coefficients. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0025] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0026] 1) Reconfigurable Intelligent Surface (RIS) is a revolutionary wireless communication technology that dynamically controls the reflection, refraction, or scattering characteristics of electromagnetic waves through software programming, thereby "intelligently reconfiguring" the wireless propagation environment. It consists of a large number of low-cost, low-power passive subwavelength reflective units, each of which can independently control the phase, amplitude, and even polarization state of the electromagnetic wave, achieving precise manipulation of the incident signal.

[0027] 2) Integrated Sensing and Communication (ISAC) is a technology that integrates communication and sensing functions into the same wireless system, using the same spectrum, hardware and signal resources to achieve synergistic effects between information transmission and environmental perception.

[0028] 3) Non-Orthogonal Multiple Access (NOMA) is a wireless communication technology whose core lies in breaking the traditional "single lane" limitation and building a "multi-lane parallel" transmission mode, enabling multiple devices to transmit data simultaneously at the same frequency.

[0029] 4) Successive Interference Cancellation (SIC) is a key technology used in communication systems to suppress multi-user interference or inter-symbol interference. It is widely used in scenarios such as Non-Orthogonal Multiple Access (NOMA) and MIMO systems.

[0030] With the development of sixth-generation mobile communication technology (6G), Integrated Communication and Sensing (ISAC) has become a core technology due to its resource-sharing advantages. Non-orthogonal multiple access (NOMA) can improve spectrum efficiency, and reconfigurable smart surfaces (RIS) can solve the occlusion problem. Research on the integration of these three technologies has begun, but existing solutions still have limitations. The following details the three closest existing technologies and their shortcomings.

[0031] The first existing technical solution considers a RIS-assisted NOMA-ISAC system and proposes a targeted system performance optimization algorithm. The core is the joint optimization of three key parameters: active beamforming of the base station, power allocation coefficients for NOMA users, and passive beamforming of the RIS. The optimization objective is to maximize the minimum beammap gain of the sensing target, while satisfying the minimum service quality rate constraints for communication users, the maximum transmit power constraints of the base station, and the RIS phase shift unity mode constraints. The second existing technical solution proposes a hybrid NOMA-OMA architecture RIS-ISAC system. In the considered system, the authors optimize the intra-cluster power allocation, base station active beamforming, and RIS passive phase shift stepwise. The optimization objective is to maximize the minimum beammap gain of the sensing target, while satisfying the minimum rate threshold for communication users, the base station transmit power budget, and the RIS reflection unit phase range constraints. The third existing technical solution considers a NOMA-ISAC system, with the optimization objective being to maximize the weighted sum of communication throughput and effective sensing power, while satisfying the minimum user rate constraints, sensing target power difference constraints, constant base station single-antenna power constraints, and upper limit constraints on the cross-correlation of sensing signals.

[0032] However, existing technical solutions have the following drawbacks: 1) Existing technologies do not construct an optimization framework centered on "communication-perception weighting and performance maximization" for RIS-assisted NOMA-ISAC systems, and cannot simultaneously meet the requirements of adaptability to urban occlusion scenarios and flexible balance of synesthetic performance across multiple scenarios: On the one hand, some solutions do not introduce RIS and only build NOMA-ISAC systems without RIS, failing to solve the non-line-of-sight propagation problem caused by building occlusion in urban environments; on the other hand, even if some solutions combine RIS and NOMA to build ISAC systems, their optimization objectives only focus on communication or perception alone, without establishing a weighted collaborative optimization mechanism for communication and perception. They cannot improve communication throughput through weight adjustment in communication-priority scenarios, nor can they quantify the impact of perception improvement on communication in perception-priority scenarios, and they cannot dynamically adapt to the synesthetic needs of different scenarios, ultimately making it difficult to achieve optimal overall synesthetic performance of RIS-assisted NOMA-ISAC systems.

[0033] 2) Existing technologies have multiple limitations in RIS phase shift optimization methods and engineering scenario adaptability, which together lead to performance loss and difficulties in industrial implementation: On the one hand, RIS phase shift optimization methods have inherent defects. Current research mostly relies on semidefinite relaxation (SDR) techniques to transform RIS phase shift matrix optimization into a semidefinite programming (SDP) problem. If the optimization result has a non-uniform rank, it will lead to a significant drop in system performance, but no better RIS phase shift optimization algorithm has been proposed to avoid this problem. On the other hand, the engineering adaptability is seriously deviated from reality. It assumes that the base station obtains perfect channel state information (CSI) and ignores the estimation error caused by multipath. It also assumes that the RIS phase shift can be continuously adjusted. This conflicts with the characteristic that the hardware only supports 2-4 bit discrete quantization, which ultimately seriously restricts the actual performance of the system and the feasibility of engineering implementation.

[0034] To overcome the communication building obstruction problem in urban environments and to meet the requirements of adaptability to urban scenarios and flexible trade-offs in multi-scenario sensing performance, this application proposes a RIS-assisted NOMA-ISAC system and corresponding resource optimization methods to solve the link reliability problem under urban obstruction and the problem of flexible trade-offs in multi-scenario sensing performance, ultimately improving the system's practicality and adaptability in urban environments.

[0035] To address the aforementioned technical issues, this application proposes a RIS-assisted NOMA-ISAC system framework and a joint resource optimization method based on "auxiliary variable transformation + alternating optimization (AO) + penalty term continuous convex approximation (SCA)". By coordinating active beamforming at the base station and passive phase shifting by the RIS, it achieves improved link reliability in urban environments and flexible trade-offs in multi-scenario sensing.

[0036] This application provides a RIS-assisted communication-sensing integrated system, including: An ISAC base station has dual functions of communication signal transmission and radar target perception, and is equipped with a uniform linear array; A reconfigurable smart surface, consisting of multiple passive reflective units, is deployed between a base station and a communication user to modulate the phase of reflected electromagnetic waves in order to construct a virtual line-of-sight link between the base station and the communication user. K communication users employ a non-orthogonal multiple access protocol and demodulate received signals using a serial interference cancellation mechanism. The decoding order is determined by the equivalent channel gain sorting assisted by the reconfigurable smart surface. L sensing targets; The base station transmits a composite signal of communication and sensing signals; the communication signal uses non-orthogonal multiple access multiplexing in the power domain; the sensing signal is orthogonal to the communication signal; and the reflective unit of the reconfigurable smart surface supports discrete phase modulation.

[0037] like Figure 1 As shown, based on the above system, this application embodiment also provides a resource optimization allocation method, specifically including the following steps: Step S1: Construct the original optimization problem with the goal of maximizing the weighted sum of communication performance and sensing performance, and with constraints such as the maximum transmit power of the base station, the minimum signal-to-interference-plus-noise ratio of each communication user, the minimum signal-to-interference-plus-noise ratio of each sensing target, and the discrete phase of the reconfigurable smart surface. Step S2: Introduce auxiliary variables to equivalently transform the nonlinear objective function in the original optimization problem into a linear form, thus obtaining the transformed optimization problem; Step S3: Using an alternating optimization method, the transformed optimization problem is decomposed into an auxiliary variable optimization sub-problem, a base station beamforming optimization sub-problem, and a reconfigurable smart surface phase shift optimization sub-problem; Step S4: Iteratively solve the auxiliary variable optimization sub-problem, the base station beamforming optimization sub-problem, and the reconfigurable smart surface phase shift optimization sub-problem until the objective function value converges, and obtain the optimal resource allocation result.

[0038] The solutions of the embodiments of this application will be described in detail and explained below with reference to specific application examples.

[0039] (1) System Model This embodiment designs a RIS-NOMA-ISAC system for urban environments (such as intelligent transportation and urban IoT). The core components and deployment rules are as follows: The ISAC base station is deployed at the origin, using an M-element uniform linear array (ULA), possessing dual functions of communication signal transmission and radar target perception, with a maximum transmit power constraint of P. The RIS is deployed at (10, 10, 5) m, consisting of a uniform planar array (UPA) composed of N passive reflective elements, supporting m-bit discrete phase modulation, with a fixed amplitude reflection coefficient of 1 (only the focusing phase affects system performance), used to construct a virtual line-of-sight link between the base station and remote users, solving the problem of building obstruction in urban areas. There are K communication users (CUs), randomly and uniformly distributed in the intersection area of ​​a "ring with an inner radius of 30 m and an outer radius of 40 m centered on the base station" and a "circle with a radius of 20 m centered on the RIS," using a serial interference cancellation (SIC) mechanism to demodulate the NOMA signal. There are a total of L target objects (STs) and J interference sources, all distributed in a "ring with the base station as the center and an inner radius of 10 m / outer radius of 15 m" (short-range deployment to ensure direct line-of-sight links between the base station and the STs).

[0040] 1.1) Channel Modeling Assume all wireless power channels are affected by large-scale path loss and small-scale fading, and experience independent Rician fading. Rician fading is related to the base station and the Rician fading of the first wireless channel. k The perfect channel between each device is modeled as follows: , (1) in, and These are respectively RIS and base station and RIS and the first k Path loss model between devices. and Represent the Rician fading components, which respectively satisfy: , (2) in, This represents the Rice coefficient of the corresponding channel, with a value set to 2 dB. and This represents the line-of-sight (Los) component of the corresponding channel, where: (3) (4) Represents the array steering vector. and These represent the azimuth and elevation angles of the line-of-sight (Los) path associated with RIS, respectively. It is the departure angle associated with the base station. and These represent the non-line-of-sight (NLoS) components of the corresponding channel, each element being an independent and identically distributed circularly symmetric complex Gaussian random variable with zero mean and 1 variance. In actual urban deployments, ISAC-BS struggles to obtain perfect CSI. Based on minimum mean square error (MMSE) estimation, the relationship between the real channel and its ideal perfect channel can be expressed as: (5) 1.2) Base station transmitted signal model To achieve the dual functions of user communication and radar sensing, the base station transmits a vector signal. This includes communication signal vectors. and radar detection signal vector They respectively satisfy and Furthermore, to reduce interference between signals, it is assumed that the communication signal and the sensing signal are uncorrelated, i.e. .set up and Let represent the communication beamforming matrix and the radar beamforming matrix, respectively. Then, the composite signal transmitted by the base station can be represented as: (6) in and satisfy , This represents the maximum transmit power of the base station.

[0041] 1.3) Subsystem Model Communication Subsystem: Due to obstructed line-of-sight path, a RIS is deployed between the ISAC base station and the user to establish a virtual line-of-sight path. The signal received by the k-th user can be written as: (6) in, It is the base station to the first under RIS assistance k The equivalent channel of each device. The phase shift matrix of RIS can be written as ,in as well as , respectively representing the first n The phase shift and amplitude coefficient of each reflective element. As mentioned above, since this embodiment focuses on the impact of the RIS phase shift on system performance, the amplitude coefficient is set to 1. Indicates the first kThe additive white Gaussian noise (AWGN) received by a device. In the system considered in this embodiment, serial interference cancellation (SIC) technology is used to suppress inter-user interference. During this process, the decoding order of each communication user (CU) is determined by the magnitude of its received channel gain. Assume that the received channel gains of each communication user satisfy For the k-th (k < K) communication user, it will first detect and cancel the interference generated by communication users with weaker channel gains (j < k), and at the same time regard the signals of all communication users with stronger channel gains (j > k) as interference. Therefore, the signal-to-interference-plus-noise ratio (SINR) when the k-th communication user decodes its own signal is: . (7) At the same time, the j-th communication user with a stronger channel gain needs to preferentially decode the signal of the k-th communication user with a weaker channel gain. Therefore, the signal-to-interference-plus-noise ratio (SINR) when this strong-gain user decodes the signal of the k-th communication user is: . (8) Based on this, the communication signal-to-interference-plus-noise ratio (SINR) of the k-th communication user can be expressed as: . (9) It should be noted that for the K-th communication user (with the strongest channel gain), it can cancel the interference of all other users through SIC technology. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the K-th communication user is .

[0042] This embodiment uses the normalized average communication rate to quantitatively evaluate the communication performance of the considered ISAC system, and its expression is: , (10) Sensing subsystem: The sensing signal reaches the sensing target (ST) through the direct link, and then the echo signal returns along the same path. In an urban scenario, the echoes generated by the reflection of surrounding objects in the sensing area will also reach the integrated communication and sensing base station (ISAC-BS), which will directly reduce the sensing accuracy. Therefore, such objects are called interference sources in this embodiment.

[0043] In the system considered in this embodiment, it is assumed that there are J interference sources, and the transmission delay between different propagation paths is ignored. Under the above assumptions, the radar echo signal received by the ISAC-BS can be expressed as: , (11) Where, and respectively represent the azimuth angles of the l-th sensing target and the j-th interference source; the array steering vector can be expressed as . , This includes round-trip path loss. and complex reflection coefficient . Let be the additive white Gaussian noise (AWGN) received by the base station. Based on this, the sensing signal-to-interference-plus-noise ratio (SINR) of the l-th sensing target can be expressed as: (12) in, and Since the sensing SINR determines the detection and localization performance, it is essential to ensure that the sensing SINR is greater than a threshold. Perceptual mutual information (MI) is a core metric for measuring perceptual performance. Using Sylvester's determinant theorem, perceptual mutual information (MI) can be expressed as: (13) Normalized average perceptual mutual information is defined as the ratio of "actual average perceptual mutual information" to "perceptual accuracy based on the minimum perceptual threshold". This metric is used to quantify the improvement in perceptual performance, and its expression is: (14) Where represents the minimum perceived signal-to-interference-plus-noise ratio (SINR).

[0044] 1.4) Optimization problem Having completed the above definitions, this embodiment aims to jointly optimize the communication beamforming matrix. Radar beamforming matrix RIS phase shift matrix To maximize the weighted sum of the average normalized communication rate and the perceived mutual information. The mathematical expression of the optimization problem is (P1): (13) Constraint C1 is the transmit power limit of the Integrated Communication and Sensing Base Station (ISAC-BS); constraints C2 and C3 are the minimum communication signal-to-interference-plus-noise ratio (SINR) and minimum sensing signal-to-interference-plus-noise ratio (SINR) guarantees for each communication user (CU) and each sensing target (ST), respectively; constraint C4 is the discrete unit mode constraint of the RIS phase shift.

[0045] (2) Joint resource optimization allocation method In the optimization problem (P1) under consideration, there are three parameters to be optimized: the communication beamforming matrix, the sensing beamforming matrix, and the phase shift matrix of the RIS. Since C2, C3, and C4 are non-convex constraints, and the optimization parameters are highly coupled, this embodiment proposes a hierarchical solution framework of "auxiliary variable transformation + alternating optimization (AO) + penalty-based continuous convex approximation (penalty-based SCA algorithm)," decomposing the original problem into three independently solvable subproblems. These three subproblems gradually approach the optimal solution through alternating iterations. The specific implementation process of the algorithm is as follows: 2.1) Objective function transformation The core obstacle to solving the original optimization problem (P1) lies in the fact that the objective function contains logarithmic terms related to communication and sensing, and the serial interference cancellation (SIC) mechanism of the NOMA protocol introduces a "minimum operator," which, nested with the logarithmic terms, forms a strongly nonlinear structure. Furthermore, the coupling between variables further increases the difficulty of solving the problem. To address this issue, this invention performs an equivalent linearization transformation of the objective function based on the following two lemmas.

[0046] a) Key Lemma: Lemma 1: For any ,function satisfy The maximum value of the function is given below: .

[0047] Lemma 2: For any ,function satisfy The maximum value of the function is given below: .

[0048] Proof: Since the first and second lemmas have a symmetric structure, taking the first lemma as an example, the second lemma can be derived in the same way. Because Calculate its second-order partial derivatives, given that it is continuously differentiable within its domain: It can be known that... Since the function is concave, its maximum value occurs at stationary points where the first partial derivative is zero. Let its first partial derivative be zero. Solve the station Substituting the stationary point into the original function yields the maximum value. Q.E.D.

[0049] b) Linearization of the objective function Based on Lemma 1 and Lemma 2, we introduce auxiliary variables: Auxiliary variables on the communication side: as well as .

[0050] Perception-side auxiliary variables: as well as .

[0051] Based on Lemma 1, It can be rewritten as: (15) in, as well as: . Based on Lemma 2, It can be rewritten as: (16) in, ,as well as: . Substituting equations (15) and (16) into the original objective function, and combining them with the Sion minima theorem, the original optimization problem can be transformed into its linear equivalent form (P2): (17) in, And the lower triangular matrix used to correspond to the NOMA decoding order This transformation eliminates the nonlinear obstacles introduced by the NOMA minimum operator and the logarithmic term, but the problem (P2) still requires further decomposition due to constraints on nonconvexity and variable coupling. The alternating iterative method used to solve the transformed problem is described below.

[0052] 2.2) Alternating Iteration (AO) Method a) Subproblem 1: Optimizing auxiliary variables Fixed communication beamforming matrix, sensing beamforming matrix, and RIS reflection phase matrix In the future, it can be obtained directly from and and The optimal solution: (18) b) Sub-problem 2: Optimizing the communication beamforming matrix and the sensing beamforming matrix Fixed auxiliary variables and the reflection phase matrix of RIS The optimization problem can then be transformed into (P3): (19) in, as well as The new variable substitution is denoted as . , , and The nonconvexity of problem (P3) stems solely from the communication SINR constraint (C2) and the sensing SINR constraint (C3). A linear transformation converts (C2) into convex equivalent forms (C8) and (C9): (20) Similarly, (C3) is transformed into its convex equivalent form (C10) by a linear transformation: .(twenty one) After performing an equivalent transformation, the objective function of the optimization problem (P3) can be rewritten as (P3)': (twenty two) To handle the non-convex constraints C11 and C12, this invention employs a continuous convex approximation (SCA) algorithm based on a penalty term. Specifically, since the matrix is ​​positive semi-definite and has a rank of one, we can obtain: and ( This represents the i-th eigenvalue of the matrix. (This represents the largest eigenvalue of the matrix). Therefore, constraints C11 and C12 are equivalent to the following two expressions: ,(twenty three) in, It is a penalty factor. However, the introduction of the penalty term causes the objective function to become non-convex again. To solve this problem, this embodiment introduces the SCA method to transform it into a convex function. At feasible points... , At each location, respectively , By performing a first-order Taylor expansion, we can derive their upper bound expressions as follows: ,(twenty four) At this point, problem (P3)' can be transformed into problem (P3)'': (25) At this point, the original optimization problem has been transformed into a Quadratic Semidefinite Programming (QSDP) problem, which can be efficiently solved using a standard convex optimization solver. After the algorithm converges, the optimal solution is obtained through eigenvalue decomposition (EVD). and .

[0053] c) Subproblem 3: Optimizing the phase shift matrix of RIS Fixed auxiliary variables, communication beamforming matrix and sensing beamforming matrix In the future, in order to optimize The optimization problem for the objective can be rewritten as (P4): (26) in, Note ,make , ,but It can be rewritten as: (where satisfying) , By substituting variables, the optimization problem (P4) can be transformed into (P4)': (27) Constraint C13 corresponds to the original constraint C4. Similar to problem (P3), the nonconvexity of problem (P4) mainly stems from its constraints. For constraint C2, based on the variable substitutions described above, it can be transformed into constraints C15 and C16: (28) For constraint C14, the SCA algorithm based on the penalty term is used to handle its nonconvexity. Since this algorithm is completely identical to the one used to solve constraints C11 and C12, its detailed derivation will not be repeated here. After equivalent transformation, problem (P4) can be rewritten as: (29) in As a penalty factor, ; for The feasible point in the first-order Taylor expansion. At this point, the original optimization problem has been transformed into a quadratic semidefinite relaxation (QSDR) problem, which can be efficiently solved using a standard convex optimization solver. The optimal solution can be obtained through eigenvalue decomposition (EVD). .

[0054] However, the phase angle considered in this embodiment is discretized using m-bit quantization. Therefore, it is necessary to further map the above optimal solution to the discrete domain using the nearest neighbor mapping (NNP) method to obtain the optimal solution. Afterwards, Perform NNP operations on each element to obtain the mapped vector. ;right By performing diagonalization, the optimal result can be obtained. The expression is: (30) Iterative optimization: Under the overall framework of the alternating optimization (AO) algorithm, the auxiliary variables, communication beamforming matrix, and sensing beamforming matrix are alternately optimized with the RIS phase shift matrix until the objective function value of problem (P2) converges.

[0055] (3) Simulation results In this embodiment, simulation analysis verifies the effectiveness of the proposed resource optimization allocation method in improving the performance of the system's communication-perception weighted sum, and compares it with the scheme based on deep reinforcement learning (DRL) algorithm (S1), the scheme based on semidefinite relaxation (SDR) algorithm, the scheme that does not use RIS to construct the virtual line-of-sight path between the base station and the communication user, and the scheme that does not use the NOMA protocol. In the Deep Reinforcement Learning (DRL) algorithm-based scheme, the system architecture is consistent with the proposed scheme (RIS+NOMA+ISAC), but Deep Reinforcement Learning (DRL) is used to optimize system performance, and its result is used as an approximate theoretical upper bound of system performance. In the Semi-definite Relaxation (SDR) algorithm-based scheme, the optimization of the communication beamforming matrix and radar beamforming matrix is ​​to transform the corresponding optimization problem into a semi-definite programming problem (SDP) through Semi-definite Relaxation (SDR) and then solve it. The optimization of other parameters adopts the method mentioned in this invention. For the scheme that does not use RIS to construct the virtual line-of-sight path between the base station and the communication user, RIS is not deployed and communication between the base station and the CU is directly achieved. After optimization algorithm adaptation and adjustment, it is consistent with the proposed scheme and is used to verify the compensation effect of RIS on the obstructed link. For the scheme that does not use the NOMA protocol, NOMA and its corresponding SIC are not used for interference cancellation during communication. After optimization algorithm adaptation and adjustment, it is consistent with the proposed scheme and is used to verify the cooperative gain of NOMA in the system.

[0056] The simulation experiment parameters are set as follows: the total number of communication users is 4, the total number of sensing targets is 3, the total number of interference sources is 12, and the RIS is a uniform planar array composed of 32 elements.

[0057] Figure 2 The proposed algorithm has a synesthesia tradeoff coefficient. and The iterative convergence curves are shown, with the horizontal axis representing the number of iterations and the vertical axis representing the system's synesthetic weighted objective function value. The dashed line represents the approximate upper bound obtained by the DRL method represented by scheme S1. Simulation results show that the proposed algorithm converges quickly under both weight configurations, and the objective function value stabilizes after approximately 10 iterations, meeting the low latency requirements of urban systems. Furthermore, the performance of the proposed algorithm after convergence is close to the theoretical upper bound. At that time, the performance of the proposed scheme reached approximately 97.12% of the theoretical upper limit; When the proposed scheme achieves a performance close to 97.91% of the theoretical upper limit, it proves that the proposed algorithm has excellent optimization accuracy.

[0058] Figure 3 Different synesthesia tradeoff coefficients Below are the inductive weighted performance curves for each scheme, with the horizontal axis representing the communication weight. The vertical axis represents the synesthetic weighted performance value. As can be seen from the simulation graph, the proposed scheme in this embodiment exhibits a monotonically increasing trend and is comprehensively superior to the benchmark scheme.

[0059] (4) Advantages and beneficial effects Current research in this field mainly focuses on the pairwise integration of RIS and ISAC, and the pairwise integration of NOMA and ISAC, with very little research on the synergistic potential of RIS, NOMA, and ISAC. Even those few studies that have constructed a RIS-NOMA-ISAC system framework have not explored the joint optimization of communication awareness within this framework, resulting in poor adaptability to practical applications. Furthermore, most current research on RIS transforms the optimization problem of the RIS phase shift matrix into a semidefinite programming (SDP) problem using semidefinite relaxation (SDR) techniques, and then solves it using convex optimization tools. However, when the rank of the optimization result is not uniform, it may lead to a significant performance degradation. Considering the current state of research, this application proposes a RIS-assisted NOMA-ISAC system framework and a corresponding resource allocation method. This application has the following main advantages: 1) This application proposes a RIS-assisted NOMA-ISAC system. By deploying RIS to construct a virtual line-of-sight link, it completely solves the problem of base station-user direct link interruption caused by urban building obstruction. Through NOMA's power domain multiplexing and serial interference cancellation (SIC) technology, it supports massive concurrent user access. Simultaneously, relying on the sensing resource sharing characteristics of ISAC, it avoids hardware and spectrum redundancy. This architecture provides an integrated solution for 6G urban scenarios, filling the gap in existing research on performance optimization of multi-technology fusion systems.

[0060] 2) This application achieves a flexible trade-off between communication and sensing performance. Existing ISAC systems mostly adopt a "single-objective optimization" approach: either optimizing communication rate with sensing performance as a constraint, or improving the sensing signal-to-noise ratio with communication performance as a constraint. This approach fails to dynamically adjust resource allocation strategies according to the scenario (e.g., prioritizing communication during peak hours and prioritizing sensing during off-peak hours in intelligent transportation). This research constructs a RIS-assisted NOMA-ISAC system and uses the weighted sum of communication and sensing performance as the optimization objective, flexibly adapting to various task requirements in urban occlusion scenarios and significantly improving the system's adaptability.

[0061] 3) For RIS phase shift, this study considers discrete phase modulation which is closer to engineering applications. On this basis, the continuous convex approximation (SCA) algorithm based on penalty terms is used to avoid the rank loss problem in solving the non-convex phase shift constraint into a semi-definite programming (SDP) problem.

[0062] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0063] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0064] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0065] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0066] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0067] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0068] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0069] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0070] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0073] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application 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 the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0074] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A RIS-assisted integrated communication and sensing system, characterized in that, include: An ISAC base station has dual functions of communication signal transmission and radar target perception, and is equipped with a uniform linear array; A reconfigurable smart surface, consisting of multiple passive reflective units, is deployed between a base station and a communication user to modulate the phase of reflected electromagnetic waves in order to construct a virtual line-of-sight link between the base station and the communication user. K communication users employ a non-orthogonal multiple access protocol and demodulate received signals using a serial interference cancellation mechanism. The decoding order is determined by the equivalent channel gain sorting assisted by the reconfigurable smart surface. L sensing targets; The base station transmits a composite signal of communication and sensing signals; the communication signal uses non-orthogonal multiple access multiplexing in the power domain; the sensing signal is orthogonal to the communication signal; and the reflective unit of the reconfigurable smart surface supports discrete phase modulation.

2. The system according to claim 1, characterized in that, The optimization objective of the system is to maximize the weighted sum of communication performance and sensing performance, wherein the communication performance is characterized by the normalized average communication rate, and the sensing performance is characterized by the normalized average sensing mutual information; the optimization variables include the communication beamforming matrix of the base station, the sensing beamforming matrix, and the phase shift matrix of the reconfigurable smart surface; the constraints include: the maximum transmit power constraint of the base station, the minimum signal-to-interference-plus-noise ratio constraint for each communication user, the minimum signal-to-interference-plus-noise ratio constraint for each sensing target, and the discrete unit mode constraint of the reconfigurable smart surface.

3. A resource optimization allocation method for the system as described in claim 1 or 2, characterized in that, Includes the following steps: Step S1: Construct the original optimization problem with the goal of maximizing the weighted sum of communication performance and sensing performance, and with constraints such as the maximum transmit power of the base station, the minimum signal-to-interference-plus-noise ratio of each communication user, the minimum signal-to-interference-plus-noise ratio of each sensing target, and the discrete phase of the reconfigurable smart surface. Step S2: Introduce auxiliary variables to equivalently transform the nonlinear objective function in the original optimization problem into a linear form, thus obtaining the transformed optimization problem; Step S3: Using an alternating optimization method, the transformed optimization problem is decomposed into an auxiliary variable optimization sub-problem, a base station beamforming optimization sub-problem, and a reconfigurable smart surface phase shift optimization sub-problem; Step S4: Iteratively solve the auxiliary variable optimization sub-problem, the base station beamforming optimization sub-problem, and the reconfigurable smart surface phase shift optimization sub-problem until the objective function value converges, and obtain the optimal resource allocation result.

4. The method according to claim 3, characterized in that, In step S2, based on the maximum point lemma of concave functions, auxiliary variables related to the communication signal-to-interference-plus-noise ratio (SINR) and the sensing signal-to-interference-plus-noise ratio (SINR) are introduced, and the logarithmic summation form in the original optimization problem is equivalently transformed into a combination of linear functions and auxiliary variables.

5. The method according to claim 3, characterized in that, The base station beamforming optimization sub-problem includes: fixing the auxiliary variables and the reconfigurable smart surface phase shift matrix, and jointly optimizing the communication beamforming matrix and the sensing beamforming matrix of the base station; Specifically, non-convex communication signal-to-interference-plus-noise ratio (SIR) constraints and sensing SIR constraints are transformed into convex constraints through linear matrix inequalities; and a continuous convex approximation algorithm based on penalty terms is used to process the rank-one non-convex constraints of the beamforming matrix, transforming the subproblem into a quadratic positive definite programming problem to be solved.

6. The method according to claim 3, characterized in that, The reconfigurable smart surface phase shift optimization sub-problem includes: fixing the auxiliary variables, the communication beamforming matrix, and the sensing beamforming matrix, and optimizing the phase shift matrix of the reconfigurable smart surface; Specifically, the non-convex phase shift constraint is transformed into an equivalent form through variable substitution; and a continuous convex approximation algorithm based on penalty terms is used to process the rank-one non-convex constraint of the substituted variables, transforming the subproblem into a quadratic positive semidefinite relaxation problem for solution.

7. The method according to claim 6, characterized in that, In the reconfigurable smart surface phase shift optimization subproblem, after obtaining the optimal continuous phase shift matrix, the optimal continuous phase shift matrix is ​​mapped to a discrete phase set through the nearest neighbor mapping method to obtain a discretized reconfigurable smart surface phase shift matrix.

8. The method according to claim 3, characterized in that, The original optimization problem constructed in step S1 also considers imperfect channel state information, which is modeled based on minimum mean square error estimation.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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