A novel signal separation algorithm based on compressed sensing for wideband spectrum sensing in cognitive radio networks

In cognitive radio networks, since cognitive terminals use a shared wideband frequency spectrum for data transmissions, they are susceptible to malicious denial-of-service attacks, where adversaries try to corrupt communication by actively transmitting interference signals. To address this issue, in this paper, we propose a novel signal separation algorithm based on compressed sensing, which can not only recover the entire spectrum but also separate mixed occupying signals. Specifically, the proposed algorithm is executed following three steps: i each cognitive terminal attempts to recover all signals over an entire wideband spectrum employing the compressed sensing technique; ii all cognitive terminals send their recovered signals to the fusion center where a wavelet edge detection method is adopted to locate the spectrum edges of these signals and then divide the entire spectrum into several sub-bands; iii the fusion center separates its received signals on each spectrum sub-band into different categories according to their features. Both analytical and simulation results indicate that this novel compressed-sensing-based algorithm can effectively separate wideband signals at a low cost and combat interference of the malicious terminals in cognitive radio networks as well. Copyright © 2013 John Wiley & Sons, Ltd.