Mutual Coupling-Aware Localization for RIS-Assisted ISAC Systems
Alireza Fadakar, Musa Furkan Keskin, Hui Chen, Henk Wymeersch · IEEE Transactions on Cognitive Communications and Networking · 2025
Integrated sensing and communication (ISAC) is recognized as a promising approach to address the growing spectrum requirements for seamless sensing and communication. This paper investigates the deployment of reconfigurable intelligent surface (RIS) in ISAC systems under line-of-sight (LoS) obstructions, addressing the limited attention given to mutual coupling (MC) among RIS elements and its impact on localization performance. We tackle the joint estimation of the 3D location of a single-antenna user equipment (UE) and MC coefficients in challenging multipath and LoS-blocked environments. To enhance MC estimation, we extend our analysis to scenarios where signals from multiple UE locations are available, leveraging the stability of MC values over extended time intervals. Our methodology encompasses several key steps: first, we estimate the delay using the multiple signal classification (MUSIC) algorithm to mitigate multipath effects; second, we employ an efficient MC-unaware maximum likelihood (ML) approach for initial 2D angle-of-departure (2D-AOD) estimation; third, we propose a novel closed-form solution for the initial estimation of MC coefficients relying on a scattering matrix-based realistic MC modeling; and finally, we introduce a low-complexity alternating optimization algorithm for the joint refinement of the 2D-AODs and MC values. Simulation results demonstrate the effectiveness of the proposed method, outperforming classical MC-unaware ML techniques.