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

TL;DR

This paper introduces a RIS-assisted semantic-aware vehicular edge computing framework that employs a PPO-based hybrid optimization scheme to significantly reduce latency in IoV applications.

Contribution

It presents a novel joint optimization approach combining RIS, semantic communication, and PPO-based hybrid decision-making for vehicular edge computing.

Findings

Reduces average latency by 40-50% compared to GA and QPSO.

Maintains low latency in congested scenarios with up to 30 vehicles.

Validates superiority of the proposed framework through simulations.

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.