Computer Science > Machine Learning
arXiv:2603.09082 (cs)
[Submitted on 10 Mar 2026]
Title:PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
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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.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2603.09082 [cs.LG] |
| (or arXiv:2603.09082v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09082
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View a PDF of the paper titled PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing, by Wei Feng and 3 other authors
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