Cooperative Spectrum Sharing in Cognitive Radio Networks with Upper Confidence Bounds

Abstract

According to Federal Communication Commission (FCC), more than 70% of the available spectrum is not utilized optimally. Cognitive radio is a better technique to fulfill the utilization of radio frequency spectrum. We consider the problem of cooperative spectrum sharing among a primary user and multiple secondary users, where the primary user selects a proper set of secondary users to serve as the cooperative relays for its transmission. Most previous works are focused on developing complex algorithms which may not be fast enough for real-time variations such as channel availability and/or assume perfect information about the network. Instead, we develop a learning mechanism for a PU to enable CSS in a strongly incomplete information scenario with low computational overhead. Our mechanism is based on a Distributed Algorithm, enhanced with
the concept of Upper Confidence Bound (UCB). This algorithm can be extended to include more sophisticated features while maintaining its desirable properties such as low computational overhead and fast speed of convergence.