An adaptive sensing period algorithm in cognitive radio networks

Spectrum sensing is a key requirement in cognitive radio networks. Given a licensed channel, the desirable level of detection quality can be achieved by physical-layer sensing algorithms. Furthermore, given multiple licensed channels, more available spectrum opportunities can be found through efficient MAC-layer sensing schemes. An important issue on MAC-layer sensing is how often to sense the availability of licensed channels. To resolve this issue, we focus on how to maximize utilization of spectrum opportunities by optimizing sensing periods of licensed channels. Specifically, an adaptive sensing period algorithm is proposed to realize the automatic adjusting of sensing periods based on the channel-usage model of licensed users. Simulations are used for performance evaluation. It is demonstrated that our proposed algorithm always has steady performance, regardless of the number of channels sensed. Simulation results also illustrate the significant performance improvement of our proposed algorithm over fix sensing period method. For the simulated scenarios, the proposed algorithm utilizes 15~20% more opportunities than fix sensing period method.