A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks

Abstract

Vehicular ad hoc networks (VANETs) are wireless networks formed of moving vehicle stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimizing channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based medium access control protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that “learn” how to act optimally in a network in order to maximize their achieved packet delivery and minimise bandwidth wastage. Additionally, via a collective contention estimation mechanism, which we embed on the Q-learning agent, faster convergence, higher throughput, and short-term fairness are achieved.