Multi-coset sampling and reconstruction of signals: Exploiting sparsity in spectrum monitoring
2013 (English)Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 1-5 p.
matrix algebra;signal reconstruction;signal sampling;frequency 700 MHz to 3000 MHz;multicoset sampling;optimum sampling pattern;reconstruction matrix;sampling pattern search algorithms;sampling pattern selection;signal reconstruction;spectrum analyzer;spectrum monitoring sparsity;Abstracts;Iron;Scattering;Wideband;Condition number;Multicoset sampling;Sampling pattern selection;Sparsity;Spectrum monitoring
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-203727ISI: 000341754500287OAI: oai:DiVA.org:kth-203727DiVA: diva2:1082349
21st European Signal Processing Conference (EUSIPCO 2013)
QC 201703162017-03-162017-03-162017-03-16Bibliographically approved