Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method
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摘要: 同时透射和反射可重构智能表面(STAR-RIS)能够创建全空间智能无线电环境，有效提高无线通信系统性能，具有广阔的研究潜力。因此，该文提出一种大规模STAR-RIS辅助的近场通感一体化(ISAC)方法，并对感知目标3维参数估计的克拉美罗界(CRB)进行优化。首先，搭建近场系统模型，分别推导基站、STAR-RIS、通信用户、感知目标与传感器之间的导向矢量。其次，通过设计基站发射波束成形矩阵、发射信号协方差矩阵和STAR-RIS透射反射系数，实现感知性能最优化。再次，针对非凸优化问题利用半正定松弛方法进行求解。仿真结果表明了所提出ISAC方案的有效性，以及近场额外距离自由度所带来的定位性能优势。Abstract:
Objective The growing demand for advanced service applications and the stringent performance requirements envisioned in future 6G networks have driven the development of Integrated Sensing and Communication (ISAC). By combining sensing and communication capabilities, ISAC enhances spectral efficiency and has attracted significant research attention. However, real-world signal propagation environments are often suboptimal, making it difficult to achieve optimal transmission and sensing performance under harsh or dynamic conditions. To address this, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) enable a full-space programmable wireless environment, offering an effective solution to enhance wireless system capabilities. In large-scale 6G industrial scenarios, STAR-RIS panels could be deployed on rooftops and walls for comprehensive coverage. As the number of reflecting elements increases, near-field effects become significant, rendering the conventional far-field assumption invalid. This paper explores the application of large-scale STAR-RIS in near-field ISAC systems, highlighting the role of near-field effects in enhancing sensing and communication performance. It highlights the importance of incorporating near-field phenomena into system design to exploit the additional degrees of freedom provided by large-scale STAR-RIS for improved localization accuracy and communication quality. Methods First, near-field ISAC system is formulated, where a large-scale STAR-RIS assists both sensing and communication processes. The theoretical framework of near-field steering vectors is applied to derive the steering vectors for each link, including those from the Base Station (BS) to the STAR-RIS, from the STAR-RIS to communication users, from the STAR-RIS to sensing targets, and from sensing targets to sensors. Based on these vectors, a system model is constructed to characterize the relationships among transmitted signals, signals reflected or transmitted via the STAR-RIS, and received signals for both communication and sensing.Next, the Cramér-Rao Bound (CRB) is then derived by calculating the Fisher Information Matrix (FIM) for three-dimensional (3D) parameter estimation of the sensing target, specifically its azimuth angle, elevation angle, and distance. The CRB serves as a theoretical benchmark for estimation accuracy. To optimize sensing performance, the CRB is minimized subject to communication requirements defined by a Signal-to-Interference-plus-Noise Ratio (SINR) constraint. The optimization involves jointly designing the BS precoding matrices, the transmit signal covariance matrices, and the STAR-RIS transmission and reflection coefficients to balance accurate sensing with reliable communication. Since the joint design problem is inherently nonconvex, an augmented Lagrangian formulation is employed. The original problem is decomposed into two subproblems using alternating optimization. Schur complement decomposition is first applied to transform the target function, and semidefinite relaxation is then used to convert each nonconvex subproblem into a convex one. These subproblems are alternately solved, and the resulting solutions are combined to achieve a globally optimized system configuration. This two-stage approach effectively reduces the computational complexity associated with high-dimensional, nonconvex optimization typical of large-scale STAR-RIS setups. Results and Discussions Simulation results under varying SINR thresholds indicate that the proposed STAR-RIS coefficient design achieves a lower CRB root than random coefficient settings ( Fig. 2 ), demonstrating that optimizing the transmission and reflection coefficients of the STAR-RIS improves sensing precision. Additionally, the CRB root decreases as the number of Transmitting-Reflecting (T-R) elements increases in both the proposed and random designs, indicating that a larger number of T-R elements provides additional degrees of freedom. These degrees of freedom enable the system to generate more targeted beams for both sensing and communication, enhancing overall system performance.The influence of sensor elements on sensing accuracy is further analyzed by varying the number of sensing elements (Fig. 3 ). As the number of sensing elements increases, the CRB root declines, indicating that a larger sensing array improves the capture and processing of backscattered echoes, thereby enhancing the overall sensing capability. This finding highlights the importance of sufficient sensing resources to fully exploit the benefits of near-field ISAC systems.The study also examines three-dimensional localization of the sensing target under different SINR thresholds (Fig. 4 ,Fig. 5 ). Using Maximum Likelihood Estimation (MLE), the proposed method demonstrates highly accurate target positioning, validating the effectiveness of the joint design of precoding matrices, signal covariance, and STAR-RIS coefficients. Notably, near-field effects introduce distance as an additional dimension in the sensing process, absent in conventional far-field models. This additional dimension expands the parameter space, enhancing range estimation and contributing to more precise target localization. These results emphasize the potential of near-field ISAC for meeting the demanding localization requirements of future 6G systems.More broadly, the findings highlight the significant advantages of employing large-scale STAR-RIS in near-field settings for ISAC tasks. The improved localization accuracy demonstrates the synergy between near-field physics and advanced beam management techniques facilitated by STAR-RIS. These insights also suggest promising applications, such as industrial automation and precise positioning in smart factories, where reliable and accurate sensing is essential.Conclusions A large-scale STAR-RIS-assisted near-field ISAC system is proposed and investigated in this study. The near-field steering vectors for the links among the BS, STAR-RIS, communication users, sensing targets, and sensors are derived to construct an accurate near-field system model. The CRB for the 3D estimation of target location parameters is formulated and minimized by jointly designing the BS transmit beamforming matrices, the transmit signal covariance, and the STAR-RIS transmission and reflection coefficients, while ensuring the required communication quality. The nonconvex optimization problem is divided into two subproblems and addressed iteratively using semidefinite relaxation and alternating optimization techniques. Simulation results confirm that the proposed optimization scheme effectively reduces the CRB, enhancing sensing accuracy and demonstrating that near-field propagation provides an additional distance domain beneficial for both sensing and communication tasks. These findings suggest that near-field ISAC, enhanced by large-scale STAR-RIS, is a promising research direction for future 6G networks, combining increased degrees of freedom with high-performance integrated services. -
1 优化过程算法
初始化参数：${\nu ^l}$, ${\eta ^l}$, ${{\boldsymbol{\varLambda}} ^l}$；设置外层收敛精度$0 < \chi < 1$，迭代
次数初始化$m = 1$，最大迭代次数为${M_{{\text{max}}}}$；内层收敛精度
$0 < c < 1$，迭代次数为$u$，最大迭代次数为${U_{{\text{max}}}}$，惩罚因子更
新参数为$0 < d < 1$；初始化h$\left( {{\nu ^0}} \right)$；(1) While$\left| {h\left( {{\nu ^m}} \right) - h\left( {{\nu ^{m - 1}}} \right)} \right| \ge \chi $ or $m \le {M_{{\text{max}}}}$ do (2) $u = 1$ (3) While $\left| {{\text{tr}}{{({{\boldsymbol{E}}^{ - 1}})}^u} - {\text{tr}}{{({{\boldsymbol{E}}^{ - 1}})}^{u - 1}}} \right| \ge c$ or $u \le {U_{{\text{max}}}}$ do (4) 固定${\varepsilon ^u}$，求得$ \mathcal{S}\mathcal{P}1 $的最优解$\tilde \tau $并更新${\left( {\tilde \tau } \right)^u}$； (5) 固定${\left( {\tilde \tau } \right)^u}$，求得$ \mathcal{S}\mathcal{P}2 $的最优解$\tilde \varepsilon $并更新${\left( {\tilde \varepsilon } \right)^u}$； (6) 设置迭代次数$u = u + 1$； (7) End while (8) If $h\left( {{\nu ^m}} \right) \le 0.95h\left( {{\nu ^{m - 1}}} \right)$ then (9) 将${\left( {\tilde \varepsilon } \right)^u}$和${\left( {\tilde \tau } \right)^u}$代入更新${\bar {\boldsymbol{O}}^m}$，
${\eta ^m} = {\eta ^{m - 1}},{{\boldsymbol{\varLambda}} ^m} = {{\boldsymbol{\varLambda}} ^{m - 1}} + \dfrac{1}{\eta }{\bar {\boldsymbol{O}}^m}$；(10) else (11) ${{\boldsymbol{\varLambda}} ^m} = {{\boldsymbol{\varLambda}} ^{m - 1}},{\eta ^m} = d{\eta ^{m - 1}}$； (12) 设置迭代次数$m = m + 1$,更新$h\left( {{\nu ^m}} \right) = {\left\| {{{\bar {\boldsymbol{O}}}^m}} \right\|_\infty }$； (13) End while 
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