Distributed Compressive Sensing: Performance analysis with diverse signal ensembles

Sung Hsien Hsieh, Wei Jie Liang, Chun Shien Lu, Soo Chang Pei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of performance for MMVs, however, is derived to be the same with that for single MV (SMV) because the characteristics of signal ensembles are ignored. In this work, we introduce a new factor called "Euclidean distances between signals" for the performance analysis of a deterministic signal model under MMVs framework. We show that, by taking the size of signal ensembles into consideration, MMVs indeed exhibit better performance than SMV. Although our concept can be broadly applied to CS algorithms with MMVs, the case study conducted on a well-known greedy solver, called simultaneous orthogonal matching pursuit (SOMP), will be explored in this paper. We show that the performance of SOMP, when incorporated with our concept by modifying the steps of support detection and signal estimations, will be improved remarkably, especially when the Euclidean distances between signals are short. The performance of modified SOMP is verified to meet our theoretical prediction.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1324-1328
Number of pages5
ISBN (Electronic)9780992862671
DOIs
Publication statusPublished - 2017 Oct 23
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 2017 Aug 282017 Sep 2

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period17-08-2817-09-02

All Science Journal Classification (ASJC) codes

  • Signal Processing

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