Dynamic Spectrum Sensing in Cognitive Radio Networks Using Compressive Sensing

In this paper, we propose a compressive sensing-based dynamic spectrum sensing algorithm for a cognitive radio network. The algorithm assumes the knowledge of initial energies in occupied channels and by using a number of wideband filters as a sensing matrix and l - 1 minimization-based dynamic detection algorithm, iteratively determines the change in occupancy of channels. The advantages of such an algorithm include reduced number of filters than in previously used algorithms and a better performance at low SNRs. The performance of the algorithm is studied by varying different parameters involved and the results are shown. We demonstrate that the algorithm is effective and robust to noise.