Centralized cooperative spectrum sensing from sub-Nyquist samples for Cognitive Radios

Cognitive Radio (CR) challenges the traditional task of spectrum sensing with requirements of reliability, efficiency and real-time. Sub-Nyquist sampling has been considered for this task in order to cope with the sampling rate bottleneck of the wideband signals a CR usually deals with, by exploiting their multiband structure. However, communication signals suffer from fading and shadowing effects that affect a single CR´s performance. In this paper, we consider collaborative spectrum sensing by a network of CRs, each sharing an observation matrix derived from sub-Nyquist samples of their respective received signal with a fusion center. Exploiting the fact that all received signals share a joint support, equal to that of the transmitted signal, the fusion center recovers it by combining the measurements of the different CRs. We present two joint reconstruction algorithms, Block Sparse Simultaneous Orthogonal Matching Pursuit (BSOMP) and Block Sparse Simultaneous Iterative Hard Thresholding (BSIHT), that adapt the original OMP and IHT to both block sparse and matrix (simultaneous) inputs. Simulations show that our algorithms outperform a collaborative scheme based on hard decisions, namely the union of the supports recovered by each CR individually, demonstrating that cooperation between CRs via measurement fusion improve their performance.