Wideband Spectrum Reconstruction with Multicoset Sub-Nyquist Sampling and Collision Classification

This paper proposes an improved method for reconstructing wideband sparse spectrum. We utilize a multicoset setup based on time delay. The simple multicoset setup is more suitable for practical implementation in comparison to more sophisticated sub-Nyquist systems. We first introduce the general reconstruction model that solves for a fixed number of variables. We employ a simple machine learning technique to classify the aliased sub-Nyquist bins into two categories. The classification method reduces the reconstruction time by decreasing the number of combinations and variables needed for resolving the signals. The saving in solution time is significant at low occupancy levels. Furthermore, the approach is robust against higher noise levels, because although the classification accuracy decreases as SNR decreases, the reduction in the accuracy of the classifier does not adversely affect the overall detection. We define detection performance metrics and provide simulation results to demonstrate the effectiveness of our approach.