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It has been known that load unaware channel selection in 802.11 networksresults in high level interference, and can significantly reduce the networkthroughput. In current implementation, the only way to determine the trafficload 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 channelshave to be measured, which is costly and can cause unacceptable communicationinterruptions between the AP and the stations. In this paper, we propose alearning based approach which aims to find the channel with the minimum load bymeasuring only limited number of channels. Our method uses Gaussian ProcessRegressing to accurately track the traffic load on each channel based on theprevious measured load. We confirm the performance of our algorithm by usingexperimental data, and show that the time consumed for the load measurement canbe reduced up to 46% compared to the case where all channels are monitored.

Load-aware Channel Selection for 802.11 WLANs with Limited Measurement