A Spatial Learning Algorithm for IEEE 802.11 Networks

The success of dynamic spectrum access through simple listen-before-talk etiquettes has paved the way for opening up the spectrum. However, many problems still remain in these networks. Due to the complex nature of IEEE 802.11 networks, for instance, optimizing these networks regarding power, rate and carrier sense threshold remains a very tough challenge. In this paper, we introduce spatial learning. This new optimization algorithm for IEEE 802.11 networks employs learning to find an optimal combination of power, rate and carrier sense threshold. It is assumed that nodes behave selfishly and are only interested in optimizing their own throughput. Extensive network simulations show that spatial learning performs better than the state-of-the- art solution, spatial backoff, on all axes of interest: network-wide throughput, fairness and power consumption.