Talk · Procedia Computer Science · January 1, 2024

An Adaptive Contention Window using Actor-Critic Reinforcement Learning Algorithm for Vehicular Ad-hoc NETworks

Praveen Kumar, Lopamudra Hota, Biraja Prasad Nayak, Arun Kumar Sangaiah

Abstract

The MAC layer IEEE 802.11p standard’s collision avoidance mechanism is sub-optimal due to the use of a Binary Exponential Backoff (BEB). This technique has an increase in the backoff slot time to minimize the probability of subsequent collisions after the occurrence of a collision. This leads to a degradation of radio bandwidth wastage, especially when managing access to a dense number of stations. An incorrect backoff setting can also increase the likelihood of collisions and decrease network performance. For adaptive CW selection, the proposed model is based on a Reinforcement Learning (RL) Actor-Critic (AC) network to adapt the CW value by learning the VANET environment. NS-3 for VANET is utilized to test and compute the optimal CW values. The proposed AC and EAC outperform traditional BEB used in the IEEE 802.11p standard as the number of vehicles increases. The throughput is enhanced by 54% and 69% respectively for AC and Enhanced AC (EAC). Similarly, the end-to-end delay for AC is reduced by 19%, and for EAC it’s reduced by 25%.

Vehicular Ad Hoc Networks (VANETs)Wireless Networks and ProtocolsTraffic control and managementadaptive contention windowvehicular ad hoc networksIEEE 802.11p

An Adaptive Contention Window using Actor-Critic Reinforcement Learning Algorithm for Vehicular Ad-hoc NETworks

Abstract

More by Praveen Kumar