Dynamic Link Adaptation in IEEE 802.11ac: A Distributed Learning Based Approach

Abstract

High throughput wireless access networks based on IEEE 802.11ac show a significant challenge in dynamically selecting the link configuration parameters based on channel conditions due to large pool of design set, like number of spatial streams, channel bonding, guard intervals, frame aggregation and different modulation and coding schemes. In this paper, we develop a learning based approach for link adaptation motivated by the multi-armed bandit based distributed learning algorithm. The proposed link adaptation algorithm, BanditLink, explores different possible configuration options based on observing their impact over the network performance at various channel conditions. We analyze the performance of BanditLink from simulation results, and observe that it performs significantly better compared to other competing mechanisms proposed in the literature.