Multi-coset sampling and reconstruction of signals: Exploiting sparsity in spectrum monitoring

We present an analytical representation of multi-coset sampling (MCS) and implement the proposed scheme on spectrum data to analyze the effect of MCS that requires less samples. Sampling pattern (SP) selection, which is one of the most significant phases of MCS, is investigated and the effect of the SP on reconstruction matrices and reconstruction process of the signal is analyzed. Different algorithms, which aim to find the optimum SP, are presented and their performances are compared. In order to present the feasibility of the process, MCS is implemented to measurements captured by a spectrum analyzer. The wideband spectrum measurements are obtained over 700-3000 MHz. They are sub-sampled and reconstructed again, so that the RMSE values of the reconstructed signals are evaluated. Effects of the SP search algorithms on the reconstruction process are analyzed for the spectrum monitoring application.