Abstract:
Combining spectrum sensing (SS) and primary user (PU) traffic forecasting
provides a cognitive radio network 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.
