Adaptive Learning-driven Contention Window Selection for Efficient Channel Access in Vehicular Networks

Abstract

In Vehicular Ad-hoc Networks (VANETs) and major transportation systems, efficient communication protocol is vital for timely data transmission to vehicles. The dense vehicular network poses challenges to efficient channel-sharing. For the proper utilization of the available bandwidth, optimization of channel mechanisms is crucial. The proposed approach enables vehicles to dynamically tune their Contention Windows (CWs) using locally observable MAC-layer information, with the objective of jointly maximizing throughput, minimizing delay, and maintaining fair channel access. Comprehensive simulations and analysis show notable improvement in the overall network efficiency in terms of throughput, collision, and delay. The adaptiveness of the proposed algorithm guarantees flexibility to changing traffic conditions and is well-suited to the evolving Intelligent Transportation Systems (ITS). With an emphasis on high throughput, low latency, and fair channel allocation, the proposed model contributes to the advanced communication protocols for VANETs. The proposed model also highlights the significance of intelligent adaptive techniques in obtaining enhanced network performance.