Prediction of exponentially distributed primary user traffic for dynamic spectrum access

In order to fully exploit the availability of primary users´ (PUs) unused spectrum by secondary users, traffic prediction can be used to increase system throughput. In this paper we propose a PU traffic prediction algorithm based on estimated PU traffic state transition probabilities. The probabilities are obtained via constrained-time PU traffic parameters estimation assuming exponentially distributed PU ON/OFF channel utilization intervals. Moreover, we define prediction regions for the estimated parameters where optimal traffic prediction is possible. Finally, we theoretically quantify the prediction confidence as a function of the prediction time, the total estimation time period and the number of samples used for estimation.