English

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

Machine Learning 2026-03-11 v1 Networking and Internet Architecture

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.

Keywords

Cite

@article{arxiv.2603.09082,
  title  = {PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing},
  author = {Wei Feng and Jingbo Zhang and Qiong Wu and Pingyi Fan and Qiang Fan},
  journal= {arXiv preprint arXiv:2603.09082},
  year   = {2026}
}

Comments

This paper has been accepted by electronics. The source code has been released at: https://github.com/qiongwu86/PPO-Based-Hybrid-Optimization-for-RIS-Assisted-Semantic-Vehicular-Edge-Computing

R2 v1 2026-07-01T11:11:30.167Z