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This paper introduces a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework to minimize latency for Internet of Vehicles (IoV) applications. A joint optimization problem is formulated, considering offloading ratios, semantic symbols, and RIS phase shifts. The problem is solved using a two-tier hybrid scheme: Proximal Policy Optimization (PPO) for discrete decisions and Linear Programming (LP) for offloading optimization, achieving a 40-50% latency reduction compared to GA and QPSO.
PPO-based optimization slashes end-to-end latency by up to 50% in vehicular edge computing compared to traditional methods like genetic algorithms.
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.
