Conference Article

·2013

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

Hasan Basri Çelebi YTU , Lütfiye Durak-Ata YTU , Hasari Çelebi

European Signal Processing Conference

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.

Keywords

Signal reconstruction Sampling (signal processing) Algorithm Spectrum (functional analysis) Computer science SIGNAL (programming language) Process (computing) Reconstruction algorithm Representation (politics) Wideband Spectrum analyzer Pattern recognition (psychology) Signal processing Mathematics Artificial intelligence Electronic engineering Iterative reconstruction Telecommunications Detector Engineering Physics

Subject Areas

Cognitive Radio Networks and Spectrum Sensing ·Computer Networks and Communications ·Physical Sciences
Blind Source Separation Techniques ·Signal Processing ·Physical Sciences
ECG Monitoring and Analysis ·Cardiology and Cardiovascular Medicine ·Health Sciences

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