Dynamically tuning IEEE 802.11’s contention window using machine learning

Abstract

The IEEE 802.11’s binary exponential backoff (BEB) algorithm plays a critical role in the throughput performance and fair channel allocation of IEEE 802.11 networks. In particular, one of BEB algorithm’s parameters, the Contention Window determines how long a node needs to wait before it (re)transmits data. Consequently, choosing adequate values of the Contention Window is crucial for IEEE 802.11’s performance. In this paper, we introduce a simple, yet effective machine learning approach to adjust the value of IEEE 802.11’s Contention Window based on present- as well as recent past network contention conditions. Using a wide range of network scenarios and conditions, we show that our approach outperforms both 802.11’s BEB as well as an existing contention window adjustment technique that only considers the last two transmissions. Our results indicate that our contention window adaptation algorithm is able to deliver consistently higher average throughput, lower end-to-end delay, as well as improved fairness.