Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

TL;DR

This paper proposes a deep reinforcement learning approach to optimize resource allocation in MEC-enabled Internet of Vehicles networks, effectively managing network slicing, channel, and power allocation to meet diverse application needs.

Contribution

Introduces a model-free DRL method for joint resource and slice management in IoV, addressing complex NP-hard optimization problems with a practical, robust solution.

Findings

DRL outperforms benchmark solutions in various network scenarios

Effective joint optimization of channel, power, and slice allocation

Robust performance under different network conditions

Abstract

The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS) are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and non-linear mathematical programming formulations, in this paper, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MEC-enabled IoV network based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable a better exploitation of the scarce channel resources. The considered problem addresses jointly the channel and power allocation, the slice selection and the vehicles selection (vehicles grouping). We model the problem as a single-agent Markov decision process. Then, we solve it using DRL using the well-known DQL algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.