PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing

TL;DR

A Proximal Policy Optimization-based hybrid optimization scheme that employs Proximal Policy Optimization for discrete decision-making and Linear Programming for offloading optimization reduces the average end-to-end latency by approximately 40% to 50% compared to Genetic Algorithm and Quantum-behaved Particle Swarm Optimization.

Abstract

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.

Figures (9)

• Figure S1: Framework of semantic communication system.
• Figure S2: System model diagram.
• Figure S3: Reward convergence plot of PPO.
• Figure S4: Performance comparison of average delay under varying vehicle transmission powers.
• Figure S5: Average delay of V2V and V2I links versus vehicle transmission power.