Joint Communication and Sensing in RIS-Assisted MIMO System Under Mutual Coupling

2026-01-13

AI Summary

This work addresses the commonly overlooked inter-element mutual coupling effect in reconfigurable intelligent surface (RIS)-assisted joint communication and sensing (JCAS) systems, which often leads to model inaccuracies. For the first time, the study investigates a JCAS system comprising a MIMO base station and an RIS under a physically consistent mutual coupling model, jointly optimizing the RIS reflection coefficients and base station beamforming to balance communication efficiency—measured by mutual information—and sensing accuracy—quantified via Fisher information. Focusing on a single-user, single-target scenario, a weighted-sum optimization approach is proposed, and the performance differences between monostatic and bistatic radar configurations are analyzed. Simulation results demonstrate that incorporating the mutual coupling model significantly enhances overall system performance and reveals the intrinsic trade-off between communication and sensing objectives.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Applications
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper considers a downlink Reconfigurable Intelligent Surface (RIS)-assisted Joint Communication and Sensing (JCAS) system within a physically-consistent setting, accounting for the effect of mutual coupling between RIS elements arising due to sub-element spacing. The system features a multiple-input multiple-output (MIMO) terrestrial base station (BS) and explores both monostatic and bistatic radar configurations to enable joint communication and sensing. In the monostatic configuration, both the transmitter and receiver are at the same location, while the bistatic configuration separates the transmitter and receiver spatially. System performance is evaluated using Fisher Information (FI) to quantify sensing accuracy and Mutual Information (MI) to measure communication efficiency. To achieve an optimal balance between communication and sensing, the RIS reflective coefficients and BS transmit beamforming are jointly optimized by maximizing a weighted sum of FI and MI. A novel solution approach is proposed for a single-user, single-object scenario, leveraging the mutual coupling model to enhance system realism. The impact of self-interference on sensing performance is also investigated through signal quantization. Numerical results reveal a fundamental trade-off between FI and MI and demonstrate that incorporating mutual coupling within a physically-consistent framework significantly improves both communication and sensing performance compared to conventional RIS-assisted JCAS models. Additionally, the analysis highlights how the choice of monostatic versus bistatic radar configuration affects system performance, offering valuable insights for the design of RIS-assisted JCAS systems.

Problem

• Joint Communication and Sensing
• Reconfigurable Intelligent Surface
• Mutual Coupling
• MIMO
• Fisher Information

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Joint Communication and Sensing (JCAS)
• Mutual Coupling
• Fisher Information
• MIMO