Abstract
Spectrum Sensing in wideband cognitive radio networks is considered one of the challenging issues facing opportunistic utilization of the frequency spectrum. Collaborative compressive sensing has been proposed as an effective technique to alleviate some of these challenges through efficient sampling that exploits the underlying sparse structure of the measured frequency spectrum. In this paper, we propose to model this problem as a compressive support recovery problem, and apply the adaptive Sequential Compressive Sensing (SCS) approach to recover spectrum holes. We propose several fusion techniques to apply the proposed approach in a collaborative manner. The experimental analysis through simulations shows that the proposed scheme can substantially increase the probability of spectrum hole detection as compared to traditional CS recovery approaches while using a very low sampling rate analog to information converter, and without requiring the knowledge of any statistical information about the environmental noise.