Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method

WANG Xiaoming; LI Jiaqi; LIU Ting; JIANG Rui; XU Youyun

doi:10.11999/JEIT240018

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WANG Xiaoming, LI Jiaqi, LIU Ting, JIANG Rui, XU Youyun. Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240018

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WANG Xiaoming, LI Jiaqi, LIU Ting, JIANG Rui, XU Youyun. Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240018

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Large-Scale STAR-RIS Assisted Near-Field ISAC Transmission Method

doi: 10.11999/JEIT240018

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62101274, 62371246)

Received Date: 2024-01-16
Rev Recd Date: 2024-09-06

Available Online: 2024-09-28

Abstract

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.

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References(20)

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