Mining frequent partial periodic patterns in spectrum usage data

Abstract

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.