Decentralised Learning MACs for Collision-free Access in WLANs

TL;DR

This paper proposes a nearly decentralised WLAN MAC scheme that achieves optimal throughput and collision-free operation by using learning algorithms and schedule adaptation, improving fairness and convergence speed.

Contribution

It introduces a new MAC scheme combining learning algorithms and schedule adaptation for decentralised collision-free WLAN access.

Findings

Achieves near-optimal long-run throughput.

Provides collision-free access with arbitrary station numbers.

Speeds up convergence to collision-free operation.

Abstract

By combining the features of CSMA and TDMA, fully decentralised WLAN MAC schemes have recently been proposed that converge to collision-free schedules. In this paper we describe a MAC with optimal long-run throughput that is almost decentralised. We then design two \changed{schemes} that are practically realisable, decentralised approximations of this optimal scheme and operate with different amounts of sensing information. We achieve this by (1) introducing learning algorithms that can substantially speed up convergence to collision free operation; (2) developing a decentralised schedule length adaptation scheme that provides long-run fair (uniform) access to the medium while maintaining collision-free access for arbitrary numbers of stations.