PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
This addresses latency-sensitive Internet of Vehicles applications for improved edge computing performance, but it is incremental as it builds on existing methods like PPO and semantic communication.
The paper tackles latency in RIS-assisted semantic vehicular edge computing by proposing a PPO-based hybrid optimization scheme, which reduces average end-to-end latency by 40-50% compared to GA and QPSO and maintains low latency with up to 30 vehicles.
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.