Fast and Efficient Compressive Sensing for Wideband Cognitive Radio Systems

This paper presents a Compressive Spectrum Sensing (CSS) technique for wideband Cognitive Radio (CR) system to shorten spectrum sensing interval. Fast and efficient CSS is used to detect wideband spectrum, where samples are taken at sub-Nyquist rate and signal acquisition is terminated automatically once the samples are sufficient for the best spectral recovery. To improve sensing performance we propose a new approach for sparsifying basis in context of CSS, based on Empirical Wavelet Transform (EWT) which is adaptive to the processed signal spectrum. Simulation results show that the proposed fast and efficient EWT CSS scheme outperforms the conventional Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) based schemes in terms of sensing time, detection probability, system throughput and robustness to noise.