基于 PPO 的混合优化方法用于 RIS 辅助语义车联网边缘计算
摘要
为支持动态环境和间歇性链路下的低时延感知车网 (IoV) 应用, 本文提出一种基于可重构智能表面 (RIS) 的语义感知车辆边缘计算 (VEC) 框架。该方法集成 RIS 以优化无线连接性, 通过传输语义特征来最小化时延。我们构建了综合性联合优化问题, 在卸载比例、语义符号数量以及 RIS 相位移的优化上进行联合求解。针对问题高维度和非凸性, 本文提出一种两层混合方案, 采用近端策略优化 (PPO) 进行离散决策, 采用线性规划 (LP) 进行卸载优化。 simulation results have validated the proposed framework's superiority over existing methods. Specifically, the proposed PPO-based hybrid optimization scheme reduces the average end-to-end latency by approximately 40% to 50% compared to Genetic Algorithm (GA) and Quantum-behaved Particle Swarm Optimization (QPSO). Moreover, the system demonstrates strong scalability by maintaining low latency even in congested scenarios with up to 30 vehicles.
引用
@article{arxiv.2603.09082,
title = {PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing},
author = {Wei Feng and Jingbo Zhang and Qiong Wu and Pingyi Fan and Qiang Fan},
journal= {arXiv preprint arXiv:2603.09082},
year = {2026}
}
备注
This paper has been accepted by electronics. The source code has been released at: https://github.com/qiongwu86/PPO-Based-Hybrid-Optimization-for-RIS-Assisted-Semantic-Vehicular-Edge-Computing