Multi-Step-Ahead Spectrum Prediction for Cognitive Radio in Fading Scenarios

Abstract This paper analyzes multi-step-ahead spectrum prediction for Cognitive Radio (CR) systems using several future states. A slot-based scenario is used, and prediction is based on the Support Vector Machine (SVM) algorithm. The aim is to determine whether multi-step-ahead spectrum prediction has gains in terms of reduced channel-switching and increased network throughput compared with short-term prediction. The system model is simulated in software using an exponential on-off distribution for primary-user traffic. A classical energy detector is used to perform sensing. With the help of simplifications, we present new closed-form expressions for the detection probability under AWGN and Rayleigh fading channels which allows the appropriate number of samples for these scenarios to be found. The performance of the proposed predictor is thoroughly assessed in these scenarios. The SVM algorithm had low prediction error rates, and multi-step-ahead idle-channel scheduling resulted in a reduction in channel switching by the SU of up to 51%. An increase in throughput of approximately 4% was observed for multi-step-ahead prediction with three future states. The results also show channel-switching savings can be achieved in a CR network with the proposed approach.