Integrated Communication Sensing Optimization Method and Device Based on STAR-RIS

Abstract

This application relates to an integrated communication sensing optimization method and apparatus based on STAR-RIS. The method includes: constructing a system model comprising a base station, STAR-RIS, and multiple communication users, and dividing them into different user clusters according to the transmission or reflection units where the communication users are located; constructing a waveform gain model for evaluating sensing performance; setting service quality requirements for communication users, and transforming the maximization objective of minimum waveform gain into an equivalent continuous optimization form by introducing auxiliary variables; relaxing the non-convex constraints involving beam design in the waveform gain model into a convex form using positive semidefinite programming to obtain an equivalent joint optimization problem; solving the equivalent joint optimization problem based on a convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration. This application solves the technical problem of poor sensing performance of individual targets caused by uneven network resource allocation.

Description

Technical Field

[0001] This application relates to the field of system optimization in wireless communication, and in particular to an integrated communication sensing optimization method and apparatus based on STAR-RIS. Background Technology

[0002] In wireless communication systems, beamforming technology, as a key means to improve communication quality and spectral efficiency, has been widely used in multi-antenna systems. Traditional beamforming design typically assigns a dedicated beam to each user and optimizes it according to their individual wireless channel conditions to achieve customized signal enhancement. However, this approach requires designing and maintaining multiple beam sets for different users, placing high demands on the number of RF links and hardware resources on the base station side, resulting in a significant increase in the overall system complexity and cost.

[0003] To reduce hardware overhead and improve the utilization efficiency of limited beam resources, multi-user shared beamforming schemes have emerged as an effective alternative in recent years. By jointly optimizing the active beamforming matrix shared by multiple communication users and the corresponding power allocation factors, the system's communication rate and sensing performance can be effectively improved without significantly increasing hardware overhead. On the other hand, Successive Interference Cancellation (SIC), as a core technology of Non-Orthogonal Multiple Access (NOMA) systems, can achieve efficient multiplexing of multiple users on the same time and frequency resources by performing hierarchical demodulation of user signals. However, the traditional SIC process relies on significant channel differences between users and requires the receiver to have strong multi-level signal decoding capabilities, which increases the processing complexity on the receiver side and raises the difficulty of hardware implementation.

[0004] In summary, to balance system performance and hardware complexity, there is an urgent need for a low-cost, high-efficiency beam sharing and power allocation joint optimization design scheme to improve the communication and sensing capabilities of STAR-RIS-assisted ISAC NOMA networks. Summary of the Invention

[0005] This application provides an integrated communication sensing optimization method and apparatus based on STAR-RIS to solve the technical problem of poor sensing performance of individual targets caused by uneven allocation of network resources.

[0006] Firstly, this application provides an integrated communication sensing optimization method based on STAR-RIS, comprising: constructing a system model including a base station, STAR-RIS, and multiple communication users, and dividing them into different user clusters according to the transmission unit or reflection unit where the communication users are located; constructing a waveform gain model for evaluating sensing performance based on the transmit beam design of the base station and the power allocation settings within the user clusters; setting the service quality requirements of the communication users, and converting the maximization objective of the minimum waveform gain of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables; relaxing the non-convex constraints involving beam design in the waveform gain model into a convex form through positive semidefinite programming to obtain a solvable equivalent joint optimization problem; solving the equivalent joint optimization problem based on a convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while satisfying the service quality requirements.

[0007] Secondly, this application provides an integrated communication sensing optimization device based on STAR-RIS, comprising: a first construction module for constructing a system model including a base station, STAR-RIS, and multiple communication users, and dividing them into different user clusters according to the transmission unit or reflection unit where the communication users are located; a second construction module for constructing a waveform gain model for evaluating sensing performance based on the transmit beam design of the base station and the power allocation settings within the user clusters; a first conversion module for setting the service quality requirements of the communication users, and converting the minimum waveform gain maximization objective of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables; a second conversion module for relaxing the non-convex constraints involving beam design in the waveform gain model into a convex form through positive semidefinite programming, thereby obtaining a solvable equivalent joint optimization problem; and a solution module for solving the equivalent joint optimization problem based on a convex optimization solution strategy, obtaining suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while satisfying the service quality requirements.

[0008] As an optional example, the first building module includes: a first building unit, used to establish a three-dimensional full-coverage spatial model based on the spatial location and coverage area of ​​the base station, the STAR-RIS, and each communication user, to obtain the system model; and a partitioning unit, used to partition each communication user into different user clusters based on the spatial location of each communication user and the transmission or reflection unit in which it is located, wherein each user cluster includes two communication users, and the communication user closer to the STAR-RIS in each user cluster is the near user, and the communication user farther from the STAR-RIS is the far user.

[0009] As an optional example, the second building module described above includes: a first computing unit for processing each user cluster. In the region Transmission beam and power allocation factor and Perform joint design and calculate STAR-RIS configuration The equivalent channel after that: ,in, For communication users Receive channel vector, For communication users The direct channel gain vector to STAR-RIS For STAR-RIS The interdependent matrix of transmission or reflection units in the region. The first unit is the channel gain matrix from the base station to STAR-RIS; the second building block is used to construct the waveform gain covariance matrix of the overall system model. ,in, For STAR-RIS The covariance matrix of the transmitted signal in the region, For base stations as clusters In the region Designed transmit beam vector, For the region The number of user clusters in the system; the third building block, used for each sensing direction. Construct the turning vector: ,in, For the array antenna in the direction The steering vector, For the spacing between array elements, For the signal wavelength, The first unit represents the number of array antenna elements; the second unit is used to calculate the gain of the corresponding waveform. ,in, To perceive direction The waveform gain Let the transmitted signal covariance matrix be... This is the steering vector.

[0010] As an optional example, the first conversion module mentioned above includes: a setting unit for setting the aforementioned service quality requirement conditions: ,in, For users within the cluster The signal-to-noise ratio of the received signal. For remote users The signal-to-noise ratio that can be received before the user decodes. For the final signal-to-noise ratio of distant users within the cluster, To determine the nearest user within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. To determine the distance of users within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. For base stations as clusters In the region Designed transmit beam vector, For the region Number of user clusters in This represents the maximum total power of the base station. For users within the cluster The power distribution factor, For remote users within the cluster The power allocation factor.

[0011] As an optional example, the first transformation module described above includes: a transformation unit for introducing auxiliary variables. The objective is to maximize the minimum waveform gain. Converted to an equivalent continuous optimization form: ,in, The set of transmit beam vectors designed for each cluster by the base station. This is the set of power allocation factors for users within the cluster.

[0012] As an optional example, the second transformation module described above includes: a first processing unit for relaxing the quadratic constraints of the beam matrix using a positive semidefinite programming approach. ,in, The effective signal power received by the user is close to that of the user. The total interference power received by the user is [the value of the interference]. The near-end signal power received by the distant user. For noise power, The signal power received by the remote user itself. This refers to the interference term in the signal received by the distant user from the nearby user. The total interference power experienced by the remote user; the second processing unit, used for first-order Taylor expansion: ,in, For reference point For non-convex functions The first-order Taylor linear approximation, The transmission / reflection coefficients of STAR-RIS are to be determined. The result of the previous iteration Reference point, For the original function At point Function values; total power constraints and positive semidefinite constraints: ,in, For the first End of The trace of the transmit beam matrix of each user cluster.

[0013] As an optional example, the above-mentioned solution module includes: a third processing unit for power allocation factor. and Random numbers between 0 and 1 are generated through Gaussian randomization; the fourth processing unit is used for beam covariance matrix generation. Initialize using a convex optimization problem that minimizes the total power: ,in, For the total transmitted beam, To perceive direction The corresponding equivalent directional response matrix, This is the initial directional gain threshold. and The initial power allocation factor; the fifth processing unit, used to calculate slack variables. The initial value; the sixth processing unit, used for iteratively solving suboptimal solutions, in the first... The next iteration uses a continuous convex approximation to perform a first-order Taylor expansion on the non-convex function and uses a convex optimization solution tool to solve the iterative problem. When the change in the target minimum waveform gain is lower than the threshold, the algorithm stops and outputs the suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration.

[0014] Thirdly, this application provides a storage medium storing a computer program, wherein the computer program is executed by a processor to perform the above-described STAR-RIS-based integrated communication sensing optimization method.

[0015] Fourthly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described STAR-RIS-based integrated communication sensing optimization method through the computer program.

[0016] The technical solutions provided in this application have the following advantages compared with the prior art: This application employs a system model comprising a base station, STAR-RIS, and multiple communication users, dividing them into different user clusters based on the transmission or reflection units where the communication users reside. Based on the base station's transmit beam design and the power allocation settings within the user clusters, a waveform gain model for evaluating sensing performance is constructed. Service quality requirements for communication users are defined, and by introducing auxiliary variables, the objective of maximizing the minimum waveform gain in the waveform gain model is transformed into an equivalent continuous optimization form. The non-convex constraints related to beam design in the waveform gain model are relaxed to a convex form using positive semidefinite programming, resulting in a solvable equivalent joint optimization problem. The equivalent joint optimization problem is solved using a convex optimization solution strategy to obtain the suboptimal values ​​for the transmit beam, power allocation factor, and STAR-RIS configuration. The solution proposes a method to maximize the minimum waveform gain of the aforementioned system model while satisfying the aforementioned quality of service requirements. This method involves constructing a system model including a base station, STAR-RIS, and multiple communication users, dividing them into transmission and reflection clusters based on the location of the communication users. A waveform gain model characterizing sensing capabilities is established based on joint transmit beam and power allocation design. By introducing auxiliary variables, the non-convex objective of maximizing the minimum waveform gain is transformed into a continuous optimization form. Semidefinite programming is used to convexize the beam-related non-convex constraints, forming a solvable equivalent joint optimization problem. A convex optimization solution strategy is then used to jointly solve for the transmit beam, power allocation factor, and STAR-RIS configuration, ultimately maximizing the minimum waveform gain of the system while satisfying the communication quality of service requirements. This achieves the goal of reducing hardware complexity while simultaneously considering multi-user communication and sensing performance, improving the system's beam utilization efficiency, spectral efficiency, and sensing accuracy, and thus solving the technical problem of poor sensing performance of individual targets caused by uneven network resource allocation. Attached Figure Description

[0017] 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.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0020] Figure 1 This is a flowchart of an optional STAR-RIS-based integrated communication sensing optimization method according to an embodiment of this application; Figure 2 This is a schematic diagram of the system model of an optional STAR-RIS-based integrated communication sensing optimization method according to an embodiment of this application; Figure 3 This is a schematic diagram of an optional STAR-RIS-based integrated communication sensing optimization device according to an embodiment of this application; Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments 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.

[0022] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0023] According to a first aspect of the embodiments of this application, an integrated communication sensing optimization method based on STAR-RIS is provided, optionally, as follows: Figure 1 As shown, the above method includes: S102, construct a system model that includes a base station, STAR-RIS and multiple communication users, and divide them into different user clusters according to the transmission unit or reflection unit where the communication users are located; S104, Based on the base station's transmit beam design and the power allocation settings within the user cluster, a waveform gain model is constructed to evaluate sensing performance; S106, set the service quality requirements of communication users, and transform the minimum waveform gain maximization objective of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables; S108, by relaxing the non-convex constraints related to beam design in the waveform gain model into a convex form through positive semidefinite programming, a solvable equivalent joint optimization problem is obtained; S110 solves the equivalent joint optimization problem based on a convex optimization solution strategy, obtaining suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while meeting the service quality requirements.

[0024] Optionally, in this embodiment, an integrated communication and sensing optimization method based on STAR-RIS is provided, aiming to solve the problems of high hardware complexity, difficulty in suppressing inter-user interference, and difficulty in uniformly optimizing communication and sensing performance in traditional multi-beam communication and sensing systems. This embodiment first constructs a three-dimensional full-coverage spatial model, i.e., a system model, including a dual-function base station (BS), a reconfigurable smart surface (STAR-RIS) capable of simultaneous transmission and reflection, and several communication users and sensing targets. The structure of the system model is as follows: Figure 2 As shown, based on the location and propagation path characteristics of communication users relative to STAR-RIS, they are divided into corresponding transmission region user clusters and reflection region user clusters. This clustering strategy allows for the differentiation of communication users in different spatial directions within the system structure, making subsequent beam design and power allocation processes more targeted.

[0025] Based on the constructed system model, an active transmit beam and intra-cluster power allocation mechanism for joint communication and sensing were designed. To characterize the system's sensing capability, waveform gain was introduced as a performance evaluation index, and a waveform gain model was constructed to characterize the sensing performance under multi-user shared beam conditions. This model can comprehensively reflect the reflection characteristics of targets in different directions, the spatial control characteristics of STAR-RIS, and the shaping capability of the base station's multi-antenna array, and is the foundation for sensing performance optimization.

[0026] Subsequently, to meet the Quality of Service (QoS) requirements in multi-user non-orthogonal multiple access (NOMA) communication scenarios, a minimum signal-to-interference-plus-noise ratio (SINR) constraint is set for each user, and auxiliary variables are introduced to transform the original non-convex objective of "maximizing the minimum waveform gain" into an equivalent continuous optimization objective. This variable substitution method can map the min-max type optimization problem, which is difficult to solve directly, into an equivalent form with a good mathematical structure, laying the foundation for the next step of convex optimization processing.

[0027] When dealing with non-convex constraints in the system model, semidefinite programming (SDP) is used to relax the quadratic expression in beam design, replacing the original non-convex constraints containing vector variables with semidefinite constraints on the beam covariance matrix. Through SDP relaxation, non-convex constraints containing squared and cross terms can be transformed into linear trace function constraints, thus obtaining a solvable convex joint optimization model. This relaxation strategy effectively balances the feasibility of solving with the physical meaning of the solution, enabling the simultaneous optimization of complex variables such as multi-user shared beams and base station and STAR-RIS collaborative design within a unified framework.

[0028] After obtaining the convex joint optimization model, the solution is obtained based on convex optimization strategies (such as interior-point method, iterative weighted minimization, or successive convex approximation of SCA). By updating the transmit beam matrix, power allocation factor, and STAR-RIS transmission / reflection coefficients in each iteration, a suboptimal solution that meets QoS requirements and improves the overall system perception performance can be gradually approximated. Finally, the output of this method includes: the active beamforming vector of the base station, the power allocation factor for each user, and the spatial configuration matrix of STAR-RIS. This enables the system to maximize the minimum waveform gain while ensuring successful SIC decoding for communication users, thereby improving the resolution of the sensing direction and the target reflection enhancement capability.

[0029] Optionally, in this embodiment, by constructing an integrated system model that combines communication and sensing, performance improvement is achieved in a multi-user shared beam environment, considering base station beam design, user power allocation, and STAR-RIS spatial control. Through auxiliary variable substitution and SDP relaxation, the original non-convex waveform gain maximization problem is transformed into a solvable convex optimization form, balancing solution efficiency and the physical interpretability of the results. Compared with traditional independent communication or sensing design schemes, this method significantly reduces the hardware cost of multi-beamforming, improves the system's spectrum utilization efficiency, and maximizes the minimum sensing gain while meeting multi-user QoS requirements, achieving a balance and enhancement of communication and sensing performance. Furthermore, the solution framework based on optimization theory has strong scalability, applicable to different types of RIS configurations, different bandwidth resources, and various application scenarios, possessing high engineering value and practical deployment feasibility.

[0030] As an optional example, a system model is constructed that includes a base station, STAR-RIS, and multiple communication users, and these users are divided into different user clusters based on the transmission or reflection units where they reside, including: Based on the spatial location and coverage area of ​​the base station, STAR-RIS, and each communication user, a three-dimensional full-coverage spatial model is established to obtain the system model. Based on the spatial location of each communication user and the transmission or reflection unit in which they are located, they are divided into different user clusters. Each user cluster includes two communication users. The communication user closer to STAR-RIS is the near user, and the communication user farther from STAR-RIS is the far user.

[0031] Optionally, in this embodiment, a system model is first constructed, including a base station, a STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface) capable of simultaneous transmission and reflection, and multiple communication users. By acquiring the spatial location, coverage area, propagation path, and angle information of the base station, STAR-RIS, and all communication users, a three-dimensional, fully covered spatial model is established to describe the wireless propagation environment. This three-dimensional model can accurately characterize the propagation characteristics of electromagnetic waves between the base station, STAR-RIS, and users, and provides a fundamental channel structure description for subsequent beam design, power allocation, and system performance optimization.

[0032] After constructing the system model, the location of the transmission or reflection unit corresponding to each communication user is further determined based on the spatial region where each user is located and their interaction method with STAR-RIS. The transmission and reflection units of STAR-RIS can reconstruct signals from different directions separately. By identifying whether the communication user is on the transmission or reflection side, the communication users can be divided into user groups located in the transmission unit and user groups located in the reflection unit.

[0033] Based on this, users within each end area are clustered according to their spatial distribution, forming multiple user clusters. Each user cluster contains two communication users. Users closer to STAR-RIS are classified as near users, possessing stronger effective channel gain and thus higher decoding priority in sensing and communication. Users farther from STAR-RIS are classified as far users, with relatively weaker channel conditions, requiring targeted compensation in subsequent beam design and power allocation. This "near-far user" structure creates user pairs suitable for NOMA (Non-Orthogonal Multiple Access) architectures, enabling the system to effectively utilize Continuous Interference Cancellation (SIC) mechanisms when sharing beam and power resources, thereby improving system communication and sensing performance.

[0034] As an optional example, based on the base station's transmit beam design and power allocation settings within the user cluster, a waveform gain model for evaluating sensing performance is constructed, including: For each user cluster In the region Transmission beam and power allocation factor and Perform joint design and calculate STAR-RIS configuration The equivalent channel after that:

[0035] in, For communication users Receive channel vector, For communication users The direct channel gain vector to STAR-RIS For STAR-RIS The interdependent matrix of transmission or reflection units in the region. This is the channel gain matrix from the base station to STAR-RIS; Construct the waveform gain-covariance matrix of the overall system model:

[0036] in, For STAR-RIS The covariance matrix of the transmitted signal in the region, For base stations as clusters In the region Designed transmit beam vector, For the region The number of user clusters in the system; For each sensing direction Construct the turning vector:

[0037] in, For the array antenna in the direction The steering vector, For the spacing between array elements, For the signal wavelength, This refers to the number of array antenna elements; Calculate the gain of the corresponding waveform:

[0038] in, To perceive direction The waveform gain Let the transmitted signal covariance matrix be... This is the steering vector.

[0039] Optionally, in this embodiment, a waveform gain model for evaluating the integrated sensing performance is constructed based on the base station's transmit beam design and the power allocation settings within the user cluster. Specifically, for each user cluster... area Internal transmission beam and its corresponding near-user power allocation factor and remote user power allocation factor They were jointly designed, and the STAR-RIS configuration matrix was calculated. The equivalent channel after the action. The equivalent channel is represented as:

[0040] in, For communication users Final received channel vector, Channel gain matrix between base station and STAR-RIS Dependency matrix of STAR-RIS transmission / reflection unit The direct equivalent channel gain after the combined effect.

[0041] Based on the obtained equivalent channel, a waveform gain covariance matrix is ​​further constructed to describe the overall transmission structure of the system:

[0042] in, For STAR-RIS The covariance matrix of the transmitted signal in the region, For base stations as clusters In the region The set transmission beam For the region The number of user clusters in the system.

[0043] To characterize the spatial directionality performance of the system in the perception task, for each target direction that needs to be perceived... Construct the steering vector of the array antenna:

[0044] in, For the array antenna in the direction The steering vector, For the spacing between array elements, For the signal wavelength, Given the number of antenna elements in the array, and based on this steering vector and covariance matrix, calculate the waveform gain corresponding to the sensing direction:

[0045] in, To perceive direction The waveform gain is a core indicator for measuring sensing performance. Let the transmitted signal covariance matrix be... This is the steering vector.

[0046] By jointly modeling the transmit beam, power allocation factor, and STAR-RIS transmission / reflection configuration, the constructed waveform gain model can accurately describe the energy distribution characteristics of the system in any spatial direction. Utilizing equivalent channel mapping and covariance matrix construction mechanisms, the system can simultaneously consider the reliability of the communication link and the directional gain of the sensing task, thereby achieving efficient spatial energy regulation through collaboration between the base station and STAR-RIS.

[0047] As an optional example, the conditions for setting the quality of service requirements of communication users include: Set service quality requirements:

[0048] in, For users within the cluster The signal-to-noise ratio of the received signal. For remote users The signal-to-noise ratio that can be received before the user decodes. For the final signal-to-noise ratio of distant users within the cluster, To determine the nearest user within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. To determine the distance of users within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. For base stations as clusters In the region Designed transmit beam vector, For the region Number of user clusters in This represents the maximum total power of the base station. For users within the cluster The power distribution factor, For remote users within the cluster The power allocation factor.

[0049] Optionally, in a STAR-RIS-assisted ISAC NOMA network, to ensure the reliability and stability of the system under multi-user concurrent communication and sensing tasks, strict quality of service (QoS) requirements need to be set for each communication user. Therefore, in this embodiment, minimum signal-to-noise ratio (SNR) constraints are given for the communication quality of near and far users, and communication quality assurance conditions are constructed by combining base station power limitations and intra-cluster power allocation rules. Specifically, for near users in each user cluster, the SNR of their received signal must be no less than the minimum SNR threshold calculated from their minimum target communication rate, to ensure that near users have sufficient anti-interference capability when decoding superimposed signals. Furthermore, to ensure reliable communication performance for far users, this embodiment also requires that the SNR of the signal received by far users before near users perform serial interference cancellation meets the corresponding minimum threshold, and further stipulates that the final SNR of far users after interference elimination must also reach the minimum target threshold calculated from their minimum communication rate requirement.

[0050] To ensure communication performance for each user cluster under the condition of overall system power constraints, this embodiment imposes a total transmit power upper limit constraint on the base station, ensuring that the sum of the beam vector power of all regions and all clusters does not exceed a preset maximum power. Simultaneously, to ensure the normal operation of the NOMA transmission mechanism within each cluster, the sum of the power allocation factors for near and far users within the cluster is required to be 1, thereby rationally allocating energy resources within a given total cluster power. Under these constraints, communication quality assurance for each communication user can be achieved under different regions and coverage patterns, providing the necessary physical layer constraint foundation for subsequent joint beam design, power allocation optimization, and STAR-RIS unit control, enabling the entire system to meet communication requirements while being compatible with the performance requirements of sensing tasks.

[0051] As an alternative example, transforming the objective of maximizing the minimum waveform gain of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables includes: By introducing auxiliary variables The objective is to maximize the minimum waveform gain. Converted to an equivalent continuous optimization form:

[0052] in, The base station is designed with a set of transmit beam vectors for each cluster. , the set of user power allocation factors within the cluster.

[0053] Optionally, in this embodiment, to optimize the system's sensing performance, a method is proposed to transform the goal of maximizing the minimum waveform gain into an equivalent continuous optimization form by introducing auxiliary variables. First, the process revolves around the constructed waveform gain model, which is jointly determined by the base station transmit beam, user cluster power allocation factor, and STAR-RIS spatial configuration parameters. Sensing performance can be measured by the waveform gain formed by the system in each target direction. Quantization is performed, wherein the waveform gain is determined by the covariance matrix. The quadratic form of the array steering vector is the core indicator for evaluating the energy pointing concentration of the system.

[0054] For integrated communication and sensing systems, the optimization objective is typically to maximize the minimum waveform gain across all sensing directions, i.e.:

[0055] in, This refers to the set of transmit beam vectors designed by the base station for each cluster in each region. Let be the set of power allocation factors for near and far users within the cluster. However, this objective is inherently piecewise nonlinear, making direct solution extremely difficult and hindering the construction of a convex optimization problem. Therefore, to improve the feasibility and continuity of the solution, an auxiliary variable is introduced to transform the aforementioned minimization-maximization structure into an equivalent continuous optimization problem with linear constraints.

[0056] Specifically, a non-negative auxiliary variable is introduced. The original objective is then replaced with maximizing this auxiliary variable, resulting in the following equivalent optimization form:

[0057] In this way, the originally nested "maximize minimum" problem is transformed into a single-layer optimization form, where the constraints only need to ensure that the waveform gain in all sensing directions is not less than the auxiliary variable. .because The relationship between the gain and each waveform is linear or quasi-linear. This form not only preserves the optimization meaning of the original objective, but also greatly improves the tractability of the transformed problem, laying a mathematical foundation for further cooperation with semidefinite relaxation (SDR) and convex optimization solutions.

[0058] Based on this, the constructed optimization problem can be directly coupled with multiple variables in the system, including the transmitted beam vector. Power allocation factor within the cluster , And the channel mapping matrix determined by STAR-RIS. This is achieved by applying constraints. The optimizer tends to seek a joint configuration that simultaneously enhances energy focusing capabilities in all sensing directions, thereby avoiding overall performance limitations caused by excessively low waveform gain in some directions. This approach not only achieves a balanced improvement in sensing performance but also takes into account the quality of service requirements of the communication link, bringing high flexibility to joint optimization.

[0059] Furthermore, this optimized form makes it possible to relax subsequent positive semidefinite programming. For example, when improving the transmitted beam from vector form to matrix form (such as... After this, semidefinite constraints can be directly applied during optimization, transforming the original non-convex structure into a solvable convex optimization framework. This is further enhanced by the introduction of auxiliary variables. The entire problem achieves good mathematical properties while ensuring equivalence, thus enabling the use of existing convex optimization methods to obtain an approximate optimal solution.

[0060] By introducing auxiliary variables to transform the minimum waveform gain maximization objective into a continuous optimization form, this embodiment significantly reduces the non-convexity of the optimization problem structurally. It transforms the originally difficult-to-solve "maximum-minimum" type objective into a single-layer optimization problem with linear constraints, providing a feasible path for subsequent semidefinite programming relaxation and convex solution. While maintaining the original performance objective, it makes the waveform gain distribution more balanced, avoiding sensing blind spots and thus significantly improving the overall spatial sensing performance of the system. Simultaneously, this optimization form can be naturally integrated with the joint design of transmit beam, power allocation, and STAR-RIS configuration, achieving efficient synergy between communication and sensing performance.

[0061] As an optional example, the non-convex constraints related to beam design in the waveform gain model can be relaxed to a convex form using positive semidefinite programming, resulting in a solvable equivalent joint optimization problem including: Relaxing the quadratic constraints of the beam matrix using semidefinite programming:

[0062] in, The effective signal power received by the user is close to that of the user. The total interference power received by the user is [the value of the interference]. The near-end signal power received by the distant user. For noise power, The signal power received by the remote user itself. This refers to the interference term in the signal received by the distant user from the nearby user. The total interference power experienced by the remote user; Using a first-order Taylor expansion:

[0063] in, For reference point For non-convex functions The first-order Taylor linear approximation, The transmission / reflection coefficients of STAR-RIS are to be determined. The result of the previous iteration Reference point, For the original function At point The function value; Total power constraints and positive semidefinite constraints:

[0064] in, For the first End of The trace of the transmit beam matrix of each user cluster.

[0065] Optionally, in this embodiment, to address the problems of strong coupling, non-convexity, and difficulty in direct solution of the waveform gain optimization problem in the STAR-RIS-assisted integrated communication sensing system, a method is proposed to construct a solvable equivalent optimization model through auxiliary variables and a semidefinite programming relaxation strategy. First, the original objective of "maximizing the minimum waveform gain" in the waveform gain model can be expressed as:

[0066] in To perceive direction The waveform gain For each user cluster, there is a set of beamforming matrices. This represents the set of power allocation factors for users within the cluster. Since the "maximum-minimum" structure is difficult to solve directly, this implementation introduces an auxiliary variable. The above objective is transformed into an equivalent continuous optimization form:

[0067] This transformation converts the nonlinear problem of "maximizing minimum waveform gain" into a linearly describable constraint form, making the optimization structure clearer and laying the foundation for subsequent convexity processing.

[0068] Subsequently, for the non-convex quadratic structure involving user signal, interference, and noise power constraints in the waveform gain model, a semidefinite programming (SDP) relaxation method was adopted to transform the beam design variables, which originally depended on vector form, into covariance matrix variables. The original non-convex constraints are rewritten in trace operation form, thereby introducing the following set of constraints:

[0069] in, The effective signal power received by the user is close to that of the user. The total interference power received by the user is [the value of the interference]. The near-end signal power received by the distant user. For noise power, The signal power received by the remote user itself. For interference terms in the near-user signal received by the far-user, the SDP is relaxed, and the non-convex quadratic terms originally involving the beam vector are replaced with linear trace terms, transforming the constraint from non-convex to a convex set. This represents the total interference power experienced by the remote user.

[0070] For the nonconvexity of the remaining STAR-RIS configuration variables, a first-order Taylor expansion is further applied to the nonconvex functions. Perform a linear approximation:

[0071] in For the transmission / reflection coefficient variables of the STAR-RIS unit, This serves as the reference point for the previous iteration. This step, through local convexity processing, allows the STAR-RIS configuration constraints to be linearized in each iteration, thus incorporating them into the convex optimization framework.

[0072] In addition, to ensure that the system meets the base station transmit power budget, this embodiment sets a total power constraint:

[0073] And add a positive semidefinite constraint:

[0074] This ensures that all beam covariance matrices are valid physical signals.

[0075] By introducing auxiliary variables, constraining convexity, and performing first-order linearization, this embodiment successfully transforms the original strongly non-convex waveform gain maximization problem into a structurally complete and solvable equivalent joint convex optimization problem, enabling the system to simultaneously optimize the transmit beam, power allocation factor, and STAR-RIS configuration within a unified framework.

[0076] By introducing auxiliary variables, employing semidefinite programming relaxation, and applying first-order Taylor linearization to the STAR-RIS configuration, this embodiment transforms the original non-convex, intractable joint optimization problem into a structured, convex, and iteratively solvable problem, effectively reducing the solution complexity and ensuring the algorithm's convergence. This method enables the system to significantly improve the minimum waveform gain while strictly meeting communication service quality requirements, achieving precise control over the energy distribution in the sensing direction, thereby simultaneously enhancing communication reliability and sensing performance.

[0077] As an optional example, the equivalent joint optimization problem is solved based on a convex optimization solution strategy, yielding suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, including: Power allocation factor and Generate random numbers between 0 and 1 using Gaussian randomization; Beam covariance matrix Initialize using a convex optimization problem that minimizes the total power:

[0078] in, For the total transmitted beam, To perceive direction The corresponding equivalent directional response matrix, The initial directional gain threshold. and This is the initial power allocation factor; Calculate slack variables The initial value; Iteratively solving for the suboptimal solution, in the... The next iteration uses a continuous convex approximation to perform a first-order Taylor expansion on the non-convex function and uses a convex optimization solution tool to solve the iterative problem. When the change in the target minimum waveform gain is lower than the threshold, the algorithm stops and outputs the suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration.

[0079] Optionally, in this embodiment, in order to obtain the suboptimal transmit beam, power allocation factor, and STAR-RIS configuration of the system, the equivalent joint optimization problem is first initialized. The specific calculation process includes the following steps: For users within the cluster Heyuan users Power allocation factor and Gaussian randomization is used to generate 0-1 random numbers to ensure that the initial power allocation satisfies the constraint of the total power of users within the cluster.

[0080] Base stations are clusters In the region Transmit beam matrix Initialize using a convex optimization problem that minimizes the total transmit power:

[0081] Based on the initial power allocation and beam matrix, calculate the variables used for second-order cone relaxation. Initial value:

[0082] By converting the inequalities in the constraints into equations for direct calculation, the initial point is guaranteed to satisfy the constraint conditions.

[0083] In each iteration In this paper, a first-order Taylor expansion of non-convex constraints is performed using a continuous convex approximation method:

[0084] The original non-convex function is linearized to form a solvable convex constraint. Then, the iterative optimization problem is solved using a convex optimization tool (such as CVX). .

[0085] The constraints include: power and disturbance constraints after continuous convex approximation, as well as total power constraints and positive semidefinite constraints.

[0086] Iterative process until the target minimum waveform gain is reached. The change between two consecutive iterations is lower than a set threshold. :

[0087] Once the iteration converges, the output system suboptimal solution includes: the base station transmit beam. User cluster power allocation factor and STAR-RIS transmission / reflection configuration .

[0088] Through the aforementioned initialization and iterative optimization process, this method effectively maximizes the minimum waveform gain of the system while ensuring the quality of service for communication users, achieving joint optimization of transmit beam and power allocation. Simultaneously, it enhances sensing performance through STAR-RIS configuration. This method avoids the complexity of directly solving traditional non-convex problems. By employing semi-definite relaxation and continuous convex approximation, it transforms the original problem into a solvable convex optimization problem, improving the algorithm's computational efficiency and convergence while reducing system hardware implementation complexity and energy consumption.

[0089] Instead of assigning a beam to each user, a scheme that clusters users and assigns one beam to each cluster effectively improves beam utilization efficiency and reduces hardware investment costs. By implementing SiC (Self-Induced Conversion) within each cluster instead of the traditional SiC scheme, the complexity of user decoding is significantly reduced. Given that the original problem is non-convex, second-order cone programming, semi-definite relaxation, and continuous convex approximation methods effectively decompose the original problem into a joint optimization problem instead of two alternating subproblems, and solve for a suboptimal solution to the original problem. This reduces the number of optimization problems to solve, thus lowering the algorithm's complexity. The initialization algorithm used can still be reduced to a convex optimization problem. This avoids the high complexity problem caused by traditional random initialization.

[0090] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0091] According to another aspect of the embodiments of this application, an integrated communication sensing optimization device based on STAR-RIS is also provided, such as... Figure 3 As shown, it includes: The first construction module 302 is used to construct a system model that includes a base station, STAR-RIS and multiple communication users, and divides them into different user clusters according to the transmission unit or reflection unit where the communication users are located; The second construction module 304 is used to construct a waveform gain model for evaluating sensing performance based on the base station's transmit beam design and the power allocation settings within the user cluster. The first conversion module 306 is used to set the service quality requirements of communication users and convert the minimum waveform gain maximization objective of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables. The second conversion module 308 is used to relax the non-convex constraints involving beam design in the waveform gain model into a convex form through positive semidefinite programming, thereby obtaining a solvable equivalent joint optimization problem. The solver module 310 is used to solve the equivalent joint optimization problem based on the convex optimization solution strategy to obtain the suboptimal solutions of the transmit beam, power allocation factor and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while meeting the service quality requirements.

[0092] It should be noted that the first construction module 302 in this embodiment can be used to execute step S102 in this application embodiment, the second construction module 304 in this embodiment can be used to execute step S104 in this application embodiment, the first conversion module 306 in this embodiment can be used to execute step S106 in this application embodiment, the second conversion module 308 in this embodiment can be used to execute step S108 in this application embodiment, and the solving module 310 in this embodiment can be used to execute step S110 in this application embodiment.

[0093] For other examples of this embodiment, please refer to the examples above, which will not be repeated here.

[0094] Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of this application, such as... Figure 4 As shown, it includes a processor 402, a communication interface 404, a memory 406, and a communication bus 408. The processor 402, communication interface 404, and memory 406 communicate with each other via the communication bus 408. Memory 406 is used to store computer programs; When processor 402 executes a computer program stored in memory 406, it performs the following steps: Construct a system model that includes a base station, STAR-RIS, and multiple communication users, and divide them into different user clusters according to the transmission or reflection unit where the communication users are located; Based on the base station's transmit beam design and the power allocation settings within the user cluster, a waveform gain model is constructed to evaluate sensing performance. The service quality requirements of communication users are set, and the objective of maximizing the minimum waveform gain of the waveform gain model is transformed into an equivalent continuous optimization form by introducing auxiliary variables. By relaxing the non-convex constraints related to beam design in the waveform gain model into a convex form through positive semidefinite programming, a solvable equivalent joint optimization problem is obtained. The equivalent joint optimization problem is solved using a convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while meeting the quality of service requirements.

[0095] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0096] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0097] As an example, the memory 406 described above may include, but is not limited to, the first building module 302, the second building module 304, the first conversion module 306, the second conversion module 308, and the solution module 310 of the STAR-RIS-based integrated communication sensing optimization device. Furthermore, it may include, but is not limited to, other module units of the STAR-RIS-based integrated communication sensing optimization device, which will not be elaborated further in this example.

[0098] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0099] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0100] Those skilled in the art will understand that Figure 4The structure shown is for illustrative purposes only. The device implementing the above-mentioned STAR-RIS-based integrated communication sensing optimization method can be a terminal device, such as a smartphone (e.g., Android phone, iOS phone), tablet computer, PDA, mobile Internet Device (MID), PAD, etc. Figure 4 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0101] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, ROM, RAM, disk or optical disk, etc.

[0102] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which is executed by a processor to perform the steps in the above-described STAR-RIS-based integrated communication sensing optimization method.

[0103] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0104] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0105] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned 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 several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0106] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units 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, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0108] The units described 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.

[0109] 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.

[0110] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A STAR-RIS-based integrated communication sensing optimization method, characterized in that, include: Construct a system model that includes a base station, STAR-RIS, and multiple communication users, and divide them into different user clusters according to the transmission or reflection unit where the communication users are located; Based on the base station's transmit beam design and the power allocation settings within the user cluster, a waveform gain model is constructed to evaluate sensing performance. The service quality requirements of communication users are set, and the minimum waveform gain maximization objective of the waveform gain model is transformed into an equivalent continuous optimization form by introducing auxiliary variables. The non-convex constraints related to beam design in the waveform gain model are relaxed to a convex form using positive semidefinite programming, resulting in a solvable equivalent joint optimization problem. The equivalent joint optimization problem is solved based on a convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while meeting the service quality requirements.

2. The method according to claim 1, characterized in that, The system model constructed includes a base station, STAR-RIS, and multiple communication users, and is divided into different user clusters according to the transmission or reflection unit where the communication users are located, including: Based on the spatial location and coverage area of ​​the base station, the STAR-RIS, and each communication user, a three-dimensional full-coverage spatial model is established to obtain the system model. Based on the spatial location of each communication user and the transmission or reflection unit in which they are located, they are divided into different user clusters. Each user cluster includes two communication users. The communication user closer to the STAR-RIS is the near user, and the communication user farther from the STAR-RIS is the far user.

3. The method according to claim 1, characterized in that, The waveform gain model for evaluating sensing performance is constructed based on the base station's transmit beam design and power allocation settings within the user cluster, including: For each user cluster In the region Transmission beam and power allocation factor and Perform joint design and calculate STAR-RIS configuration The equivalent channel after that: in, For communication users Receive channel vector, For communication users The direct channel gain vector to STAR-RIS For STAR-RIS The interdependent matrix of transmission or reflection units in the region. This is the channel gain matrix from the base station to STAR-RIS; Construct the waveform gain covariance matrix of the overall system model: in, For STAR-RIS The covariance matrix of the transmitted signal in the region, For base stations as clusters In the region Designed transmit beam vector, For the region The number of user clusters in the system; For each sensing direction Construct the turning vector: in, For the array antenna in the direction The steering vector, For the spacing between array elements, For the signal wavelength, This refers to the number of array antenna elements; Calculate the gain of the corresponding waveform: in, To perceive direction The waveform gain The transmitted signal covariance matrix, This is the steering vector.

4. The method according to claim 1, characterized in that, The conditions for setting the quality of service requirements of communication users include: Define the service quality requirements as follows: in, For users within the cluster The signal-to-noise ratio of the received signal. For remote users The signal-to-noise ratio that can be received before the user decodes. For the final signal-to-noise ratio of distant users within the cluster, To determine the nearest user within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. To determine the distance of users within the cluster The minimum signal-to-noise ratio threshold is calculated based on the minimum target communication rate. For base stations as clusters In the region Designed transmit beam vector, For the region Number of user clusters in This represents the maximum total power of the base station. For users within the cluster The power distribution factor, For remote users within the cluster The power allocation factor.

5. The method according to claim 1, characterized in that, The step of converting the objective of maximizing the minimum waveform gain of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables includes: By introducing auxiliary variables The objective is to maximize the minimum waveform gain. Converted to an equivalent continuous optimization form: in, The set of transmit beam vectors designed for each cluster by the base station. This is the set of power allocation factors for users within the cluster.

6. The method according to claim 1, characterized in that, The step of relaxing the non-convex constraints related to beam design in the waveform gain model into a convex form using positive semidefinite programming yields a solvable equivalent joint optimization problem, including: Relaxing the quadratic constraints of the beam matrix using semidefinite programming: in, The effective signal power received by the user is close to that of the user. The total interference power received by the user is [the power of interference]. The near-end signal power received by the distant user. For noise power, The signal power received by the remote user itself. This refers to the interference term in the signal received by the distant user from the nearby user. This represents the total interference power experienced by the remote user. Using a first-order Taylor expansion: in, For reference point For non-convex functions, The first-order Taylor linear approximation, The transmission / reflection coefficients of STAR-RIS are to be determined. The result of the previous iteration Reference point, For the original function At point The function value; Total power constraints and positive semidefinite constraints: in, For the first End of The trace of the transmit beam matrix of each user cluster.

7. The method according to claim 6, characterized in that, The equivalent joint optimization problem is solved using a convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration, including: Power allocation factor and Generate random numbers between 0 and 1 using Gaussian randomization; Beam covariance matrix Initialize using a convex optimization problem that minimizes the total power: in, For the total transmitted beam, To perceive direction The corresponding equivalent directional response matrix, This is the initial directional gain threshold. and This is the initial power allocation factor; Calculate slack variables The initial value; Iteratively solving for the suboptimal solution, in the... The next iteration uses a continuous convex approximation to perform a first-order Taylor expansion on the non-convex function and uses a convex optimization solution tool to solve the iterative problem. When the change in the target minimum waveform gain is lower than the threshold, the algorithm stops and outputs the suboptimal solutions for the transmit beam, power allocation factor, and STAR-RIS configuration.

8. An integrated communication sensing optimization device based on STAR-RIS, characterized in that, include: The first building module is used to build a system model that includes a base station, STAR-RIS and multiple communication users, and divides them into different user clusters according to the transmission unit or reflection unit where the communication users are located; The second construction module is used to construct a waveform gain model for evaluating sensing performance based on the base station's transmit beam design and the power allocation settings within the user cluster. The first conversion module is used to set the service quality requirements of communication users and convert the minimum waveform gain maximization objective of the waveform gain model into an equivalent continuous optimization form by introducing auxiliary variables. The second conversion module is used to relax the non-convex constraints related to beam design in the waveform gain model into a convex form through positive semidefinite programming, so as to obtain a solvable equivalent joint optimization problem. The solution module is used to solve the equivalent joint optimization problem based on the convex optimization solution strategy to obtain suboptimal solutions for the transmit beam, power allocation factor and STAR-RIS configuration, so that the system model maximizes the minimum waveform gain while meeting the service quality requirements.

9. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 7 through the computer program.

Images