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

• URL: http://arxiv.org/abs/2603.09082v1
• Date: Tue, 10 Mar 2026 01:47:10 GMT
• Title: PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
• Abstract summary: This paper proposes a Re Intelligent Surface (RIS)-aided semantic applications amidst dynamic environments and intermittent links.<n>The proposed PPO-based hybrid optimization scheme reduces the average end-end latency by approximately to 50%.
• Score: 16.536167168951046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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