Opportunistic channel selection based on time series prediction in cognitive radio networks

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Abstract

ABSTRACT To improve the utilisation efficiency of licensed spectrum in cognitive radio network, two new channel selection strategies are proposed for the secondary user (SU) in this paper. The proposed solution tries its best to reduce collision and switching probabilities of the SU during data transmission. By using historical information of the licensed spectrum, the SU chooses the channel with the lowest busy probability within its service time for data transmission. Time series prediction is employed to forecast the near future busy probabilities of the licensed spectrum units, and a novel time series prediction method named distance factor recursive least square is also presented. Simulations prove that the performances of the SU, which is measured by collision probability with primary user, switching probability during data transmission and throughput within limited time slots, are all significantly improved when compared with random channel selection method. Higher prediction accuracy than the conventional recursive least square and lazy learning methods is achieved by proposed time series prediction algorithm when tested by voice traffic data and Lorenz time series. Copyright © 2013 John Wiley & Sons, Ltd.

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