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 Cramér-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.