  It has been known that load unaware channel selection in 802.11 networks
results in high level interference, and can significantly reduce the network
throughput. In current implementation, the only way to determine the traffic
load on a channel is to measure that channel for a certain duration of time.
Therefore, in order to find the best channel with the minimum load all channels
have to be measured, which is costly and can cause unacceptable communication
interruptions between the AP and the stations. In this paper, we propose a
learning based approach which aims to find the channel with the minimum load by
measuring only limited number of channels. Our method uses Gaussian Process
Regressing to accurately track the traffic load on each channel based on the
previous measured load. We confirm the performance of our algorithm by using
experimental data, and show that the time consumed for the load measurement can
be reduced up to 46% compared to the case where all channels are monitored.
