Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

TL;DR

This paper proposes a deep learning-based predictive beamforming method for vehicular V2I networks that reduces training overhead and improves sum-rate by implicitly learning channel features without explicit tracking.

Contribution

It introduces a novel unsupervised deep learning framework using convolutional LSTM networks for predictive beamforming in ISAC systems, bypassing explicit channel tracking.

Findings

Achieves near-optimal sum-rate performance close to genie-aided upper bound.

Reduces signaling overhead significantly compared to traditional methods.

Ensures sensing performance while maximizing communication throughput.

Abstract

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.