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

2025-01-02

AI Summary

To address the low energy efficiency, poor real-time performance, and insufficient intelligent coordination in Integrated Sensing and Communication (ISAC) for V2X networks, this paper proposes a lightweight Markov Decision Process (MDP)-based beamforming framework. We innovatively embed Spiking Neural Networks (SNNs) into an Actor-Critic deep reinforcement learning architecture and introduce policy pruning to enable real-time joint optimization of beamforming and power allocation—relying solely on the current sensing state. To the best of our knowledge, this is the first ISAC approach that simultaneously enhances communication rate, sensing accuracy, and energy efficiency. Simulation results demonstrate that, compared to baseline methods, the proposed framework reduces energy consumption by 42%, improves communication rate by 28%, and decreases sensing error by 35%, significantly enhancing real-time responsiveness and robustness in dynamic V2X environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Beamforming Strategies
• ISAC Optimization
• Energy Efficiency in V2X

Innovation

• Pulse Neural Network
• Deep Reinforcement Learning
• ISAC System Optimization