Q-Learning based Collision Avoidance for 802.11 Stations with Maximum Requirements

• Chang Kyu Lee,
• Dong Hyun Lee,
• Junseok Kim,
• Xiaoying Lei,
• Seung Hyong Rhee,

Vol. 17, No. 3, March 31, 2023
10.3837/tiis.2023.03.019, Download Paper (Free):

• 802.11 WLAN
• Q-learning
• backoff algorithm
• Contention Window
• Resource Allocation

Abstract

The IEEE 802.11 WLAN adopts a random backoff algorithm for its collision avoidance mechanism, and it is well known that the contention-based algorithm may suffer from performance degradation especially in congested networks. In this paper, we design an efficient backoff algorithm that utilizes a reinforcement learning method to determine optimal values of backoffs. The mobile nodes share a common contention window (CW) in our scheme, and using a Q-learning algorithm, they can avoid collisions by finding and implicitly reserving their optimal time slot(s). In addition, we introduce Frame Size Control (FSC) algorithm to minimize the possible degradation of aggregate throughput when the number of nodes exceeds the CW size. Our simulation shows that the proposed backoff algorithm with FSC method outperforms the 802.11 protocol regardless of the traffic conditions, and an analytical modeling proves that our mechanism has a unique operating point that is fair and stable.

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