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We provide a simple and accurate analytical model for multi-cellinfrastructure IEEE 802.11 WLANs. Our model applies if the cell radius, $R$, ismuch smaller than the carrier sensing range, $R_{cs}$. We argue that, thecondition $R_{cs} >> R$ is likely to hold in a dense deployment of AccessPoints (APs) where, for every client or station (STA), there is an AP veryclose to the STA such that the STA can associate with the AP at a high physicalrate. We develop a scalable cell level model for such WLANs with saturated APand STA queues as well as for TCP-controlled long file downloads. The accuracyof our model is demonstrated by comparison with ns-2 simulations. We alsodemonstrate how our analytical model could be applied in conjunction with aLearning Automata (LA) algorithm for optimal channel assignment. Based on theinsights provided by our analytical model, we propose a simple decentralizedalgorithm which provides static channel assignments that are Nash equilibria inpure strategies for the objective of maximizing normalized network throughput.Our channel assignment algorithm requires neither any explicit knowledge of thetopology nor any message passing, and provides assignments in only as manysteps as there are channels. In contrast to prior work, our approach to channelassignment is based on the throughput metric.

Modeling Multi-Cell IEEE 802.11 WLANs with Application to Channel Assignment