Compressed Spectrum Sensing of Sparse Wideband Signals Based on Deep Learning

In cognitive radio (CR), compressed spectrum sensing (CSS) is an attractive technology for detecting spectrum holes over a large band with a sub-Nyquist sampling rate. Traditional compressed spectrum (CS) reconstruction methods exhibit poor reconstruction performance and high complexity, due to the unknown and dynamically changing sparsity order of the observed signal, the hand-crafted optimization parameters involved in solvers, and dozens or hundreds of iterations. To cope with these challenges, we propose a wideband signal CS reconstruction algorithm by merging the iterative shrinkage thresholding algorithm (ISTA) and deep learning (DL) through deep unfolding and refer to it as the ISTA deep reconstruction network (ISTADR-Net). The parameters in ISTADR-Net (e.g., regularization and step size parameters) are learned end-to-end rather than being adjusted manually. Next, we propose a convolutional neural network (CNN)-based wideband spectrum sensing (WSS) network (CNNWSS-Net), which makes full use of the sparse characteristics of the reconstructed signal in the frequency domain to improve the WSS performance. The simulation results show that the proposed CSS scheme combining ISTADR-Net and CNNWSS-Net works well in low and high compression ratio scenarios, low and high signal–to–noise ratio (SNR) regions. Compared to the traditional CSS methods and other DL-based CSS methods, the proposed CSS scheme yields superior performance in terms of the probability of detection and the probability of false alarm. Furthermore, the computational complexity and computation time of the proposed CSS scheme are reduced compared to the traditional CSS methods.