A Robust Channel Access Using Cooperative Reinforcement Learning for Congested Vehicular Networks

Abstract

Vehicular Ad-hoc Network (VANET) is an emerging technique dedicated to wireless vehicular communication to improve transportation safety by exchanging driving information between vehicles. For safety purposes, vehicles periodically broadcast a safety packet via Vehicle-to-Vehicle (V2V) communication. Accordingly, VANET safety applications demand a reliable exchange of the safety packet with high Packet Delivery Ratio (PDR), acceptable latency, and communication fairness. However, the communication performance significantly degrades due to numerous packet collisions when a large number of vehicles simultaneously access limited channel resources for the safety broadcast. In particular, the problem grows more severe in congested VANETs absent infrastructures since vehicles must control channel access using a self-adaptive scheme without external assistance. Thus, a robust and decentralized channel access protocol for VANETs is required to achieve road safety. In this paper, we propose an intelligent channel access algorithm empowered by cooperative Reinforcement Learning (RL), in which vehicles coordinate the channel access in a fully-decentralized manner. We also consider a proper interaction scheme between vehicles for enhancing the V2V safety broadcast in infrastructure-less congested VANETs. We provide evaluation results with extensive simulations according to various levels of traffic congestion. Simulations confirm the superior performance of the algorithm: the algorithm has a 20% increase in PDR compared to the latest RL-based channel access scheme. Furthermore, the algorithm satisfies the low latency requirement of VANET safety applications as well as both short-term and long-term communication fairness.