Joint Localization and Information Transfer for RIS Aided Full-Duplex Systems

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing (CS) problem. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters to mitigate the model mismatch problem. Finally, we analyze the efficacy of various CS algorithms through the Bayesian Cram ' er-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.

Automated summary

This work investigates a scenario where a reconfigurable intelligent surface (RIS) aids integrated sensing and communication (ISAC), focusing on joint localization and information retrieval. The RIS enhances reflected echoes and passive transfers information from devices to a base station (BS) via reflection modulation. The work proposes a grid-based parametric model and formulates the joint estimation problem as a compressive sensing (CS) problem, employing an expectation-maximization (EM) algorithm to tune the grid parameters and mitigate model mismatch. The effectiveness of various CS algorithms is analyzed through Bayesian Cram ' er-Rao bound (BCRB).