用深度强化学习优化车联网波束成形，省电又高效。
Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks
- 将动态交通环境建模为马尔可夫决策过程，仅凭当前感知信息决策。
- 结合脉冲神经网络，能耗降低显著，通信与感知性能均提升。
- 适合追求低功耗、高可靠性的未来智能交通系统应用。
本文提出一种面向集成感知与通信（ISAC）的车联网（V2X）网络的能量高效学习型波束成形方案。首先，将车联网环境的动态不确定性建模为马尔可夫决策过程（MDP），使路侧单元仅基于当前感知信息即可生成波束成形决策，无需频繁导频传输和大规模信道状态信息获取。随后，设计一种深度强化学习（DRL）算法，联合优化波束成形与功率分配，在高度动态场景中兼顾通信吞吐量与感知精度。为缓解传统学习方法的高能耗问题，将脉冲神经网络（SNNs）嵌入DRL框架，利用其事件驱动和稀疏激活特性显著提升能效，同时保持鲁棒性能。仿真结果表明，该方法在实现显著节能的同时，展现出更优的通信性能，具备支撑未来绿色可持续车联网系统的潜力。
原文摘要 · Abstract (English)
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.
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