PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
arXiv:2603.09082v1 Announce Type: new 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
arXiv:2603.09082v1 Announce Type: new 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.
Executive Summary
This article proposes a novel framework for Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) to support latency-sensitive Internet of Vehicles (IoV) applications. The framework integrates RIS to optimize wireless connectivity and semantic communication, minimizing latency by transmitting semantic features. A two-tier hybrid scheme is proposed, employing Proximal Policy Optimization (PPO) for discrete decision-making and Linear Programming (LP) for offloading optimization. Simulation results demonstrate the proposed framework's superiority over existing methods, reducing average end-to-end latency by approximately 40% to 50%. The system also exhibits strong scalability in congested scenarios with up to 30 vehicles.
Key Points
- ▸ The proposed framework integrates RIS to optimize wireless connectivity and semantic communication.
- ▸ A two-tier hybrid scheme is employed, combining PPO and LP for optimization.
- ▸ Simulation results show a significant reduction in average end-to-end latency compared to existing methods.
Merits
Strength in Optimization
The proposed framework effectively optimizes wireless connectivity and semantic communication, resulting in reduced latency and improved system scalability.
Demerits
Complexity of Hybrid Scheme
The two-tier hybrid scheme, incorporating PPO and LP, may be computationally intensive and require significant resources for implementation.
Expert Commentary
The article presents a promising approach to optimizing latency-sensitive IoV applications using RIS-aided semantic-aware VEC. The proposed framework's ability to reduce latency and improve system scalability is a significant advancement in the field. However, the complexity of the hybrid scheme may limit its practical implementation. Future research should focus on simplifying the optimization process and further evaluating the framework's performance under various scenarios.
Recommendations
- ✓ Future research should investigate the potential of RIS technology in other edge computing applications beyond IoV.
- ✓ The development of more efficient optimization algorithms that can handle the complexity of the hybrid scheme is recommended.
