An adaptive double thresholds scheme for spectrum sensing in cognitive radio networks

Spectrum sensing is a fundamental requirement in cognitive radio (CR) networks. In this paper, a novel spectrum sensing scheme based on energy detection is presented which enables a significant reduction in the number of samples. The sensing process involves several stages and each stage employs two thresholds that are adjusted according to the number of samples. Furthermore, an iterative algorithm is developed for obtaining the optimal number of samples at each sensing stage. As the proposed scheme requires fewer samples of signal, there will be less time for sensing and more time for data transmission which contributes to an improvement in the throughput of the CR networks. Numerical results are provided to show that the proposed sensing scheme has a clear advantage over the conventional energy detection.