用智能表面优化车联网语义计算，显著降低延迟。
PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
- 结合RIS与语义通信，分两层用PPO和线性规划联合优化。
- 相比遗传算法等方法，端到端延迟降低40%至50%。
- 适合高密度车载场景，30辆车下仍保持低延迟。
为应对动态环境和间歇性链路下的车联网（IoV）低延迟应用需求，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。该方案通过优化任务卸载比例、语义符号数量及RIS相位偏移，实现无线连接与语义通信协同优化。针对问题高维非凸特性，设计两层混合优化策略：采用近端策略优化（PPO）处理离散决策，线性规划（LP）优化卸载分配。仿真结果验证了所提框架的优越性：相比遗传算法（GA）与量子行为粒子群优化（QPSO），平均端到端延迟降低约40%至50%；系统在最多30辆车辆的密集场景中仍保持低延迟表现，展现出良好可扩展性。
原文摘要 · Abstract (English)
To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by transmitting semantic features. We formulate a comprehensive joint optimization problem by optimizing offloading ratios, the number of semantic symbols, and RIS phase shifts. Considering the problem's high dimensionality and non-convexity, we propose a two-tier hybrid scheme that employs Proximal Policy Optimization (PPO) for discrete decision-making and Linear Programming (LP) for offloading optimization. {The 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.
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