Adaptive Compressed Spectrum Sensing Based on Cross Validation in WideBand Cognitive Radio System

Abstract

This paper proposes an adaptive compressed spectrum sensing (CSS) algorithm to detect the spectral holes in wideband cognitive radio (CR) system without the a priori information on the sparsity of received signal. Utilizing Johnson–Lindenstrauss lemma and cross validation, it is proved that the recovery error of wideband analog signal can be estimated by the recovery error of testing measurements in random demodulated compressed sensing. Then, taking the recovery error of wideband analog signal estimated as a stopping rule, an adaptive CSS algorithm is proposed to detect the spectral holes in the wideband spectrum. Furthermore, the parameters are optimized in the algorithm to maximize system throughput. Finally, the numerical results show the effectiveness of the algorithm and the optimization of parameters. For a wideband channel with an unknown spectrum occupancy, this adaptive CSS can obtain the minimum sampling rate and detect the spectrum holes for CR users. Compared with the traditional CSS and two-step CSS, this adaptive CSS can reduce the sampling resource and improve the throughput in wideband cognitive radio system.