Predicting The Throughput Of Next Generation IEEE 802.11 WLANs In Dense Deployments

Abstract

Next-generation IEEE 802.11 WLANs when deployed in dense environments and complex situations, the throughput achieved is much lower than the estimated values due to interference, overlapping of channel bandwidths and contention. Throughput estimation through simulators is tedious and needs elaborate information regarding the deployment details related to overlapping BSS scenarios. With large accurate datasets of BSS deployments, it is found to be possible to approach the problem of prediction of throughput of each BSS by using well-crafted machine learning (ML) models. In this paper, we proposed three ML approaches to predict the throughput viz artificial neural networks (ANN), k-Nearest Neighbours (kNN) regression and random forest regression. The root mean square error and the mean absolute error thus calculated in each of these approaches in the given setting are promising enough to further pursue the probe for more accurate prediction models based on machine learning.