A novel reduced power compressive sensing technique for wideband cognitive radio

Wideband spectrum sensing for cognitive radio requires high rate analog to digital (A/D) converters whose power consumption is proportional to the sampling rate. In this article, we propose to use sub-Nyquist non-uniform sampling for spectrum sensing to reduce the power consumption. Since the received signal samples are correlated in the time domain, we estimate the missing samples by using the expectation-maximization (EM) algorithm. It is shown that the combined sub-Nyquist non-uniform sampling and EM algorithm consume much less power than A/D converter at the Nyquist rate making the proposed algorithm a viable low-power solution for spectrum sensing. Moreover, it is shown by simulations that the proposed sub-Nyquist rate non-uniform sampler is accurate enough to detect the edges of the estimated power spectral density.