Optimized WLAN Channel Allocation based on Gibbs Sampling with Busy Prediction using a Probabilistic Neural Network
Julian L. Webber, Abolfazl Mehbodniya, Kazuto Yano, Yoshinori Suzuki · 2019
This paper studies a channel allocation algorithm for next generation wireless local area networks (WLANs). The aim is to maintain a fairness in usage of all Wi-Fi channels in a network. A probabilistic neural network (PNN) is employed for predicting the upcoming busy-line status on each channel and this information is distributed to other access-points (APs) in the network. The probability of switching to a new less-busy channel which is modeled by a Markov chain is solved by sampling from a Gibbs distribution with low computational complexity. Simulation results show that the application layer and MAC layer transmission delays are reduced by up to 10% and 5% respectively with respect to the current IEEE 802.11ac system.