High-Resolution Wideband Spectrum Sensing Based on Sparse Bayesian Learning

Wideband spectrum sensing for cognitive radio is highly challenging because it needs to locate multiple active spectrum subbands (channels) across a large bandwidth. The high-speed Nyquist sampling involved is either technically infeasible or very expensive in implementation. In this paper, we draw on the recent development in Bayesian machine learning, and propose a new high-resolution wideband spectrum sensing method, referred to as sub-Nyquist assisted matrix sparse Bayesian learning (M-SBL). We first use multicoset sampling to significantly reduce the sampling rate. Then we develop a M-SBL method that carries out Bayesian inference from received spectrum data to learn and iteratively reconstruct a latent variable, whose significant peaks can be used to locate multiple active spectrum subbands. Simulation results indicate that the proposed method significantly outperforms conventional ones in sensing accuracy, especially at low signal-to-noise ratios or with a small number of cosets.