  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.
