Cooperative Prediction for Cognitive Radio Networks

Abstract

Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network (CRN) with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process, since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions. To overcome these problems a collaborative approach to forecasting PU traffic behaviour, that combines SS and forecasting through SU cooperation, has been proposed in this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were investigated and a number of binary prediction methods were considered (including the authors' own simple technique). Cooperative prediction performance was investigated, under various PU traffic conditions, for a group of ten SUs experiencing different channel conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise cooperative prediction error. Simulation results indicated that the accuracy of the prediction methods was influenced by the PU traffic pattern and that cooperative prediction lead to a significant improvement in prediction accuracy under most of the traffic conditions considered. However, this came at the cost of increased computational complexity. The pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement), but was also eleven times more complex than when no fusion was employed. The cooperative forecasting algorithm was found to further improve these results.

Cooperative Spectrum Prediction-Driven Sensing for Energy Constrained Cognitive Radio Networks

“…To overcome the issues of local spectrum prediction, cooperative mode of spectrum prediction are presented in [7], [17]- [19], which leverage a collaborative decision about PU existence through the fusion of the local prediction results of geographically distributed secondary users (SUs). The work in [17], [18], presents collaborative approaches for spectrum prediction under various PU traffic conditions to minimize error probability that may incur during local spectrum prediction in term of PU's activity pattern and the sensing error. The work in [7] presents a cooperative spectrum prediction model (CPM), which makes use of past sensing data.…”