  The implementation of integrated sensing and communication (ISAC) highly
depends on the effective beamforming design exploiting accurate instantaneous
channel state information (ICSI). However, channel tracking in ISAC requires
large amount of training overhead and prohibitively large computational
complexity. To address this problem, in this paper, we focus on ISAC-assisted
vehicular networks and exploit a deep learning approach to implicitly learn the
features of historical channels and directly predict the beamforming matrix for
the next time slot to maximize the average achievable sum-rate of system, thus
bypassing the need of explicit channel tracking for reducing the system
signaling overhead. To this end, a general sum-rate maximization problem with
Cramer-Rao lower bounds-based sensing constraints is first formulated for the
considered ISAC system. Then, a historical channels-based convolutional long
short-term memory network is designed for predictive beamforming that can
exploit the spatial and temporal dependencies of communication channels to
further improve the learning performance. Finally, simulation results show that
the proposed method can satisfy the requirement of sensing performance, while
its achievable sum-rate can approach the upper bound obtained by a genie-aided
scheme with perfect ICSI available.
