Skip to Main Content
In a dense WLAN environment, the signal coverage area of each access point (AP) typically has significant overlap with that of the neighboring APs. This is a problem if there are limited frequency channels. This paper presents an algorithm that can improve per-user throughput significantly, particularly for nonuniform traffic conditions. It is based on a cellular neural network model. Like a cellular neuron changing its state, based on the information of its neighboring neurons, every AP determines the best channel it should use in the next time slot, based solely on the traffic load of its neighboring APs and the channels used by them in the current time slot, but it actually switches to that channel with some fixed probability less than one. All APs in the network perform the above operation simultaneously. Computer simulations show that (1) given any traffic load distribution and any initial channel allocation, the algorithm converges to an equilibrium state in a short time, in which the overall throughput of the network is significantly improved; and (2) there exists an optimal switching probability that can minimize the time for the algorithm to reach the equilibrium state. The proposed technique has significant practical value due to its simplicity and effectiveness.