Optimal Resource Placement in 5G/6G MEC for Connected Autonomous Vehicles Routes Powered by Deep Reinforcement Learning | IEEE Conference Publication | IEEE Xplore

Optimal Resource Placement in 5G/6G MEC for Connected Autonomous Vehicles Routes Powered by Deep Reinforcement Learning

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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 More

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
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Conference Location: Daytona Beach, FL, USA

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