Channel Status Prediction using Auto-regressive and Auto-regressive Integrated Predictors over WLAN Channel

Abstract

Recently, due to the increase of huge number of wireless devices such as smartphones or sensors, mobile wireless traffic is dramatically expanding each year. Cognitive radio (CR) system has been attracted attention to improve frequency usage efficiency. CR system is a technology that enables to select multiple radio systems, grasps the congestion status of communication and selects the optimum radio system. Till now, there are many researches considering the prediction of channel occupancy ratio (COR: the ration between busy duration length to resolution period T). If the start and end points of busy/idle duration from the sensing channel spectrum can be correctly predicted, it will largely benefit the wireless system design and spectrum efficiency (SE) improvement. In this paper, we will consider such research based on auto-regressive (AR) and auto-regressive integrated (ARI) models using traffic data captured from the wireless channel near a railway station. The major idea is that the busy/idle duration length can be calculated from COR value when the resolution period T is short. The results confirm that our proposal can improve the prediction accuracy.