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

2025-08-27

AI Summary

To address the high beamforming energy consumption and reliance on frequent pilot signaling and complete channel state information (CSI) in ISAC-enabled V2X networks, this paper proposes a deep reinforcement learning (DRL) beamforming framework based on spiking neural networks (SNNs). We formulate the dynamic V2X environment as a Markov decision process (MDP) and jointly optimize beam direction and transmit power in real time using radar sensing data only—eliminating the overhead of conventional CSI acquisition. Crucially, we replace standard artificial neural networks with SNNs, leveraging their event-driven operation and sparse activation to significantly reduce computational and communication energy costs while maintaining target communication throughput and sensing accuracy. Experimental results demonstrate a 37.2% improvement in energy efficiency under high-mobility and highly dynamic scenarios, establishing a deployable, low-overhead intelligent beamforming paradigm for green and sustainable vehicular networks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Energy-efficient beamforming for ISAC-enabled V2X networks
• Reducing frequent pilot transmissions in dynamic V2X environments
• Balancing communication throughput and sensing accuracy requirements

Innovation

• DRL algorithm optimizes beamforming and power allocation
• SNNs embedded into DRL for energy efficiency
• Event-driven SNN architecture maintains robust performance