Measurement-Based Channel Management in WLANs

Wireless frequency resources are often the limiting factor for WLAN throughput. Hence, wireless channel management is needed to mitigate co-channel interference and improve channel reuse efficiency in WLANs, particularly in those with high-density access point deployment. The key challenge is to accurately predict interference and its effect on network performance. This paper presents a new interference prediction model that incorporates realistic signal and traffic measurements, and accurately estimates network performance under alternative channel assignments. Using this model, we develop a channel management algorithm that can adapt to dynamic network situations. Experiments on an indoor testbed of 35 wireless nodes demonstrate that our algorithm significantly outperforms existing channel management techniques, with an average throughput increase of about 30%.