A Novel Wavelet-Based Energy Detection for Compressive Spectrum Sensing

Wavelet transform has proved to be an attractive tool in terms of analyzing singularities and irregular structures, which can characterize irregular edges of signals. Thus, it is well motivated to apply the wavelet transform approach to wideband spectrum sensing. But the existing wavelet-based spectrum sensing schemes work under the assumption that the frequency response of the analog signal input at the sensing receiver is real. To make this method work for more types of signals, this paper develops a novel wavelet-based approach to compressive wide-band spectrum sensing. In the proposed scheme, the wide-band time domain signal is fed into a number of filters and the sub-Nyquist sampled outputs are utilized to detect the occupancy of spectrum via a wavelet-based edge detector. The filters' outputs are real and nonnegative, regardless of the style of the analog signal. Furthermore, through simply adding the measurement vectors at the fusion center, this scheme can be applied to the case of multiple cognitive radios (CRs), reducing the complexity compared with traditional joint recovery algorithms.