  This paper investigates the integrated sensing and communication (ISAC) in
vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective
beamforming design is essential which however, highly depends on the
availability of accurate channel tracking requiring large training overhead and
computational complexity. Motivated by this, we adopt a deep learning (DL)
approach to implicitly learn the features of historical channels and directly
predict the beamforming matrix to be adopted for the next time slot to maximize
the average achievable sum-rate of an ISAC system. The proposed method can
bypass the need of explicit channel tracking process and reduce the signaling
overhead significantly. To this end, a general sum-rate maximization problem
with Cramer-Rao lower bounds (CRLBs)-based sensing constraints is first
formulated for the considered ISAC system taking into account the multiple
access interference. Then, by exploiting the penalty method, a versatile
unsupervised DL-based predictive beamforming design framework is developed to
address the formulated design problem. As a realization of the developed
framework, a historical channels-based convolutional long short-term memory
(LSTM) network (HCL-Net) is devised for predictive beamforming in the
ISAC-based V2I network. Specifically, the convolution and LSTM modules are
successively adopted in the proposed HCL-Net to exploit the spatial and
temporal dependencies of communication channels to further improve the learning
performance. Finally, simulation results show that the proposed predictive
method not only guarantees the required sensing performance, but also achieves
a satisfactory sum-rate that can approach the upper bound obtained by the
genie-aided scheme with the perfect instantaneous channel state information
available.
