标题： 基于脉冲神经网络驱动的DRL的V2X网络中节能智能ISAC 显示英文标题

标题： Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL

摘要： 集成感知与通信（ISAC）已成为实现车对万物（V2X）连接、移动性和安全性的关键技术。 然而，在V2X网络的动态和不确定环境中设计高效的波束成形方案以实现精确感知并提升通信性能面临重大挑战。 虽然人工智能技术提供了有希望的解决方案，但神经网络（NNs）的高能耗特性对通信基础设施造成了巨大负担。 本工作提出了一种节能且智能的V2X网络ISAC系统。 具体而言，我们首先利用马尔可夫决策过程框架来建模V2X网络的动态和不确定性。 该框架使路边单元（RSU）能够仅依靠其当前感知状态信息来制定波束成形方案，从而无需大量导频信号和广泛的信道状态信息获取。 为了赋予系统智能并提升其性能，我们随后引入了一种基于策略剪裁技术的Actor-Critic框架的先进深度强化学习（DRL）算法，使得波束成形和功率分配策略的联合优化成为可能，从而保证通信速率和感知精度。 此外，为减轻神经网络的能耗需求，我们将脉冲神经网络（SNNs）集成到DRL算法中。 通过利用离散脉冲及其时间特性进行信息传输，SNN不仅显著降低了在ISAC辅助的V2X网络中部署AI模型的能耗，还进一步提升了算法性能。 大量的仿真结果验证了所提出方案的有效性，具有更低的能耗、更优越的通信性能和更高的感知精度。 显示英文摘要

摘要： Integrated sensing and communication (ISAC) has emerged as a pivotal technology for enabling vehicle-to-everything (V2X) connectivity, mobility, and security. However, designing efficient beamforming schemes to achieve accurate sensing and enhance communication performance in the dynamic and uncertain environments of V2X networks presents significant challenges. While AI technologies offer promising solutions, the energy-intensive nature of neural networks (NNs) imposes substantial burdens on communication infrastructures. This work proposes an energy-efficient and intelligent ISAC system for V2X networks. Specifically, we first leverage a Markov Decision Process framework to model the dynamic and uncertain nature of V2X networks. This framework allows the roadside unit (RSU) to develop beamforming schemes relying solely on its current sensing state information, eliminating the need for numerous pilot signals and extensive channel state information acquisition. To endow the system with intelligence and enhance its performance, we then introduce an advanced deep reinforcement learning (DRL) algorithm based on the Actor-Critic framework with a policy-clipping technique, enabling the joint optimization of beamforming and power allocation strategies to guarantee both communication rate and sensing accuracy. Furthermore, to alleviate the energy demands of NNs, we integrate Spiking Neural Networks (SNNs) into the DRL algorithm. By leveraging discrete spikes and their temporal characteristics for information transmission, SNNs not only significantly reduce the energy consumption of deploying AI model in ISAC-assisted V2X networks but also further enhance algorithm performance. Extensive simulation results validate the effectiveness of the proposed scheme with lower energy consumption, superior communication performance, and improved sensing accuracy.