Cooperative Wideband Spectrum Sensing Algorithm Based on Compressed Sensing Channel Energy Measurements

Compressed sensing offers a new wideband spectrum sensing scheme in cognitive radio. This paper presents a cooperative sensing scheme based on compressed sensing to sense channel energies without reconstructing the wideband spectrum. Multiple secondary users employ a number of wideband random filters to achieve channel energy measurements. A centralized fusion center is used to collect simultaneously the measurements where a novel cooperative recovery algorithm named Simultaneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized to reconstruct all the channel energies. Simulations show that the cooperative scheme only needs 20% of the required number of filters in additive white Gaussian noise channel and needs 40% in Raleigh fading channel. SSAMP algorithm outperforms the Simultaneous Orthogonal Matching Pursuit (SOMP) on both reconstruction quality and algorithm complexity.