RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling

Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes. In this paper, a joint uplink communication and imaging system is proposed for the first time, where a reconfigurable intelligent surface (RIS) is used to manipulate the electromagnetic signals for echo decoupling at the base station (BS). Aiming to enhance the transmission gain in desired directions and generate required radiation pattern in the region of interest (RoI), a phase optimization problem for RIS is formulated, which is high dimensional and nonconvex with discrete constraints. To tackle this problem, a back propagation based phase design scheme for both continuous and discrete phase models is developed. Moreover, the echo decoupling problem is tackled using the Bayesian method with the factor graph technique, where the problem is represented by a graph model which consists of difficult local functions. Based on the graph model, a message-passing algorithm is derived, which can efficiently cooperate with the adaptive sparse Bayesian learning (SBL) to achieve joint communication and imaging. Numerical results show that the proposed method approaches the relevant lower bound asymptotically, and the communication performance can be enhanced with the utilization of imaging echoes.

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