An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks

TL;DR

This paper proposes a multi-agent reinforcement learning approach for spectrum sharing in heterogeneous vehicular networks, enabling distributed, autonomous channel selection by vehicles in dynamic environments.

Contribution

It introduces a MARL-based method for distributed spectrum sharing in HetVNETs, allowing vehicles to autonomously and collaboratively select channels without centralized control.

Findings

MARL enables efficient spectrum sharing in HetVNETs

Vehicles can autonomously select channels based on local sensing

Collaborative MARL improves network performance

Abstract

This paper presents the extension of the idea of spectrum sharing in the vehicular networks towards the Heterogeneous Vehicular Network(HetVNET) based on multi-agent reinforcement learning. Here, the multiple vehicle-to-vehicle(V2V) links reuse the spectrum of other vehicle-to-interface(V2I) and also those of other networks. The fast-changing environment in vehicular networks limits the idea of centralizing the CSI and allocate the channels. So, the idea of implementing ML-based methods is used here so that it can be implemented in a distributed manner in all vehicles. Here each On-Board Unit(OBU) can sense the signals in the channel and based on that information runs the RL to decide which channel to autonomously take up. Here, each V2V link will be an agent in MARL. The idea is to train the RL model in such a way that these agents will collaborate rather than compete.