Abstract:
The paper explores customized services for Connected Autonomous Vehicles (CAVs) in Beyond 5G (B5G) and 6G networks. It proposes an optimal VNF placement solution in Edge ...Show MoreMetadata
Abstract:
The paper explores customized services for Connected Autonomous Vehicles (CAVs) in Beyond 5G (B5G) and 6G networks. It proposes an optimal VNF placement solution in Edge Computing (EC)-enabled heterogeneous networks for CAVs. The solution leverages Deep Reinforcement Learning (DRL) to allocate computing resources based on demand and network conditions intelligently. The performance evaluation compares a value-based approach, two policy-based approaches, and an iterative Integer Linear Programming (ILP) algorithm. Simulation results demonstrate that our proposed value-based DRL solution outperforms the ILP algorithm in decision-making response time and performs near-optimal in terms of cost per route and total hops per route.
Date of Conference: 02-05 October 2023
Date Added to IEEE Xplore: 06 September 2023
ISBN Information: