A sounding signal detection scheme for compressed spectrum sensing in non-sparse wideband cognitive radios

For cognitive radio (CR), compressed sensing (CS) techniques have been utilized for spectrum sensing in order to alleviate the high signal acquisition costs in the wideband regime. However, the compressed spectrum reconstruction will fail owing to the non-sparsity of the spectrum when the primary (licensed) users (PU or LU) occupy most subchannels. In this paper we study the problem of detecting spectrum holes from the non-sparse primary user signals in a wideband cognitive radio networks using compressed sensing theory. A sounding signal detection scheme and an improved analog-to-information converter (AIC) structure to obtain the sounding signals at spectrum holes through linear operation in frequency domain has been developed. Under the framework of compressed sensing, the scheme uses priori information of primary users' spectrum allocation to design matched pattern of sounding signals. Without recover of the sampling signal, it performs linear operation on sampling data in the compressed domain to retain sounding signals only in spectrum holes, using the linear arithmetic properties of DFT. Then, through back-end signal processing model, parameter estimates of non-sparse signals are directly obtained from the observed compressive sampling value. Last, a simulation experiment is designed to verify the proposed sounding signal detection method. Simulation results indicate that the method can improve the detection accuracy, reduce the complexity of reconstruction, and enhance the robustness against received signal types.