Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks: A Deep Learning Approach

TL;DR

This paper proposes a deep learning-based predictive beamforming method for vehicular ISAC networks that reduces overhead by directly predicting beamforming matrices from historical channel data, achieving near-optimal sum-rate performance.

Contribution

It introduces a convolutional LSTM network for implicit channel feature learning and direct beamforming prediction, bypassing explicit channel tracking in ISAC vehicular systems.

Findings

Achieves near upper-bound sum-rate performance.

Reduces system signaling overhead significantly.

Maintains sensing performance requirements.

Abstract

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.