Traffic Prediction from Wireless Environment Sensing

In this paper, we propose a method for predicting traffic values from spectrum measurements. The purpose is to obtain traffic information from the wireless environment for making informed decisions in order to balance traffic loads. We propose a supervised prediction scheme trained by real measurements, such that the method adapts to new radio contexts for new measurements. As a first step, features are extracted from spectrum measurements using probability distributions and a Principal Component Analysis. Then, the relevant features are selected based on a correlation criterion. In the third stage, the predictor, mapping the selected features to a traffic value, is trained using the k Nearest Neighbours algorithm. The predictor reliability is then evaluated using a cross validation. The results obtained with spectrum measurements realized on the GSM900 and the DCS1800 frequency bands show that the proposed scheme achieves a reliable traffic prediction.