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

2026-03-09

AI Summary

This work addresses the challenge of processing delay-sensitive tasks in dynamic vehicular networks, where intermittent links severely degrade performance. To tackle this issue, the authors propose a reconfigurable intelligent surface (RIS)-assisted semantic-aware edge computing framework. They formulate an end-to-end latency minimization model by jointly optimizing task offloading ratios, the number of semantic symbols, and RIS phase shifts. A two-layer hybrid optimization mechanism is devised: the upper layer employs proximal policy optimization (PPO) to handle discrete decisions, while the lower layer solves continuous variables via linear programming (LP), effectively managing the high-dimensional non-convex problem. Experimental results demonstrate that, under a high-load scenario with 30 vehicles, the proposed approach reduces average end-to-end latency by 40%–50% compared to genetic algorithm and quantum particle swarm optimization baselines, substantially enhancing system real-time performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Internet of Vehicles
• Reconfigurable Intelligent Surface
• Semantic Communication
• Edge Computing
• Latency Minimization

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Semantic Communication
• Proximal Policy Optimization (PPO)
• Vehicular Edge Computing
• Hybrid Optimization