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.