Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks

TL;DR

This paper introduces an energy-efficient, learning-based beamforming approach for ISAC-enabled V2X networks using deep reinforcement learning and spiking neural networks to optimize performance and reduce energy consumption.

Contribution

It presents a novel DRL framework with SNNs for energy-efficient beamforming in dynamic V2X environments, reducing reliance on extensive channel information.

Findings

Significant energy savings demonstrated in simulations

Improved communication throughput and sensing accuracy

Robust performance in highly dynamic scenarios

Abstract

This work proposes an energy-efficient, learning-based beamforming scheme for integrated sensing and communication (ISAC)-enabled V2X networks. Specifically, we first model the dynamic and uncertain nature of V2X environments as a Markov Decision Process. This formulation allows the roadside unit to generate beamforming decisions based solely on current sensing information, thereby eliminating the need for frequent pilot transmissions and extensive channel state information acquisition. We then develop a deep reinforcement learning (DRL) algorithm to jointly optimize beamforming and power allocation, ensuring both communication throughput and sensing accuracy in highly dynamic scenario. To address the high energy demands of conventional learning-based schemes, we embed spiking neural networks (SNNs) into the DRL framework. Leveraging their event-driven and sparsely activated architecture, SNNs significantly enhance energy efficiency while maintaining robust performance. Simulation results confirm that the proposed method achieves substantial energy savings and superior communication performance, demonstrating its potential to support green and sustainable connectivity in future V2X systems.