  Integrated sensing and communication (ISAC) and intelligent reflecting
surface (IRS) are viewed as promising technologies for future generations of
wireless networks. This paper investigates the channel estimation problem in an
IRS-assisted ISAC system. A deep-learning framework is proposed to estimate the
sensing and communication (S&C) channels in such a system. Considering
different propagation environments of the S&C channels, two deep neural network
(DNN) architectures are designed to realize this framework. The first DNN is
devised at the ISAC base station for estimating the sensing channel, while the
second DNN architecture is assigned to each downlink user equipment to estimate
its communication channel. Moreover, the input-output pairs to train the DNNs
are carefully designed. Simulation results show the superiority of the proposed
estimation approach compared to the benchmark scheme under various
signal-to-noise ratio conditions and system parameters.
