We provide a simple and accurate analytical model for infrastructure IEEE 802.11 WLANs. Our model applies if the cell radius, R, is much smaller than the distance , R_{cs} , up to which carrier sensing is effective. The condition R_{cs} >> R is likely to hold in a dense deployment of Access Points (APs) where, for every client or station (STA), there is an AP very close to the STA such that the STA can associate with the AP at a high Physical (PHY) rate. We develop a scalable cell level model for such WLANs with saturated AP and STA queues as well as for TCP-controlled long file transfers . The accuracy of our model is demonstrated by comparison with ns-2 simulations. We also demonstrate how our analytical model could be applied in conjunction with a Learning Automata (LA) algorithm for optimal channel assignment. Based on the insights provided by our analytical model, we also propose a simple decentralized algorithm which provides static channel assignments that are \textit{Nash equilibria in pure strategies for the objective of maximizing \textit{normalized network throughput in as many steps as there are channels. In contrast to prior work, our approach to channel assignment is based on the \textit{throughput metric .
We provide a simple and accurate analytical model for multi-cell infrastructure IEEE 802.11 WLANs. Our model applies if the cell radius, R, is much smaller than the carrier sensing range , R_{cs} . We argue that, the condition R_{cs} >> R is likely to hold in a dense deployment of Access Points (APs) where, for every client or station (STA), there is an AP very close to the STA such that the STA can associate with the AP at a high physical rate. We develop a scalable cell level model for such WLANs with saturated AP and STA queues as well as for TCP-controlled long file downloads . The accuracy of our model is demonstrated by comparison with ns-2 simulations. We also demonstrate how our analytical model could be applied in conjunction with a Learning Automata (LA) algorithm for optimal channel assignment. Based on the insights provided by our analytical model, we propose a simple decentralized algorithm which provides static channel assignments that are \textit{Nash equilibria in pure strategies for the objective of maximizing \textit{ normalized network throughput. Our channel assignment algorithm requires neither any explicit knowledge of the topology nor any message passing, and provides assignments in only as many steps as there are channels. In contrast to prior work, our approach to channel assignment is based on the \textit{throughput metric .
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infrastructure
multi-cell infrastructure
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distance
carrier sensing range
R
, up to which carrier sensing is effective. The
. We argue that, the
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Physical (PHY)
physical
R
cell level
cell level
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transfers
downloads
R
ns-2
ns-2
D
also
R
static
static
R
Nash equilibria in pure strategies
Nash equilibria in pure strategies
D
normalized network throughput
R
in
normalized network throughput. Our channel assignment algorithm requires neither any explicit knowledge of the topology nor any message passing, and provides assignments in only
R
throughput metric
throughput metric