Performance evaluation of distributed compressed wideband sensing for cognitive radio networks

In emerging cognitive radio (CR) networks, the first cognitive task preceding any dynamic spectrum management is the sensing and identification of spectrum holes in wireless environments. This paper develops a distributed compressed spectrum sensing approach for (ultra-)wideband CR networks. First, compressive sampling is performed at local CRs to scan the very wide spectrum at practical signal-acquisition complexity. Then, measurements or states from multiple CR detectors are fused to collect spatial diversity gain, which improves the detection quality especially under fading channels. Distributed fusion algorithms are derived to effect collaborative fusion at low communication and computation load scalable to the network size. Multiple performance metrics are identified to evaluate the proposed spectrum sensing techniques, as well as to assess those performance-determining factors in system design. Computer simulations corroborate the effectiveness of the proposed techniques in identifying and locating the spectrum hole opportunities in wireless fading environments.