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Achieving integrated sensing and communication (ISAC) via uplink transmissionis challenging due to the unknown waveform and the coupling of communicationand sensing echoes. In this paper, a joint uplink communication and imagingsystem is proposed for the first time, where a reconfigurable intelligentsurface (RIS) is used to manipulate the electromagnetic signals for echodecoupling at the base station (BS). Aiming to enhance the transmission gain indesired directions and generate required radiation pattern in the region ofinterest (RoI), a phase optimization problem for RIS is formulated, which ishigh dimensional and nonconvex with discrete constraints. To tackle thisproblem, a back propagation based phase design scheme for both continuous anddiscrete phase models is developed. Moreover, the echo decoupling problem istackled using the Bayesian method with the factor graph technique, where theproblem is represented by a graph model which consists of difficult localfunctions. 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 thatthe proposed method approaches the relevant lower bound asymptotically, and thecommunication performance can be enhanced with the utilization of imagingechoes.

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