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

Hasan Basri Celebi, Lutfiye Durak-Ata, Hasari Celebi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
Publication statusPublished - 2013
Externally publishedYes
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: 9 Sept 201313 Sept 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period9/09/1313/09/13

Keywords

  • Condition number
  • Multicoset sampling
  • Sampling pattern selection
  • Sparsity
  • Spectrum monitoring

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