Predicting coverage in wireless local area networks with obstacles using kriging and neural networks

In this paper, a new approach based on ordinary kriging is proposed to predict network coverage in wireless local area networks. The proposed approach aims to reduce the cost of active site surveys by estimating path loss at points where no measurement data is available using samples taken at other points. To include the effect of obstacles on the covariance among points, a distance measure is developed based on an empirical path loss model. The proposed approach is tested in a simulated wireless local area network. The results show that ordinary kriging is able to estimate path loss with acceptable error levels.