Contention-based learning MAC protocol for broadcast vehicle-to-vehicle communication

Abstract

Vehicle-to-Vehicle Communication (V2V) is an upcoming technology that can enable safer, more efficient transportation via wireless connectivity among moving cars. The key enabling technology, specifying the physical and medium access control (MAC) layers of the V2V stack is IEEE 802.11p, which belongs in the IEEE 802.11 family of protocols originally designed for use in WLANs. V2V networks are formed on an ad hoc basis from vehicular stations that rely on the delivery of broadcast transmissions for their envisioned services and applications. Broadcast is inherently more sensitive to channel contention than unicast due to the MAC protocol's inability to adapt to increased network traffic and colliding packets never being detected or recovered. This paper addresses this inherent scalability problem of the IEEE 802.11p MAC protocol. The density of the network can range from being very sparse to hundreds of stations contenting for access to the channel. A suitable MAC needs to offer the capacity for V2V exchanges even in such dense topologies which will be common in urban networks. We present a modified version of the IEEE 802.11p MAC based on Reinforcement Learning (RL), aiming to reduce the packet collision probability and bandwidth wastage. Implementation details regarding both the learning algorithm tuning and the networking side are provided. We also present simulation results regarding achieved message packet delivery and possible delay overhead of this solution. Our solution shows up to 70% increase in throughput compared to the standard IEEE 802.11p as the network traffic increases, while maintaining the transmission latency within the acceptable levels.