Wavelet-based compressed spectrum sensing for cognitive radio wireless networks

Abstract

Spectrum sensing is an essential functionality of cognitive radio wireless networks (CRWNs) that enables detecting unused frequency sub-bands for dynamic spectrum access. This paper proposes a compressed spectrum sensing framework by (i) constructing a sparsity basis in wavelet domain that helps compressed sensing at sub-Nyquist rates and (ii) applying a wavelet-based singularity detector on the reconstructed signal to identify available frequency sub-bands with low complexity. In particular, for the compressed sensing, an optimized Haar wavelet basis is employed to sparsely represent piecewise constant (PWC) signals which closely approximates the frequency spectrum of a sensed signal. Our simulation results show that our proposed framework outperforms existing compressed spectrum sensing methods by providing higher accuracy at lower sampling rates.