Distributed compressive sensing for correlated information sources

Jeonghun Park, Seunggye Hwang, Janghoon Yang, Kitae Bae, Hoon Ko, Dong Ku Kim

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

Abstract

The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.

Original languageEnglish
Title of host publicationBig Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings
EditorsJason J. Jung, Pankoo Kim
PublisherSpringer Verlag
Pages130-137
Number of pages8
ISBN (Print)9783319589664
DOIs
Publication statusPublished - 2017 Jan 1
Event7th International Conference on Big Data Technologies and Applications, BDTA 2016 - Seoul, Korea, Republic of
Duration: 2016 Nov 172016 Nov 18

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume194 LNICST
ISSN (Print)1867-8211

Other

Other7th International Conference on Big Data Technologies and Applications, BDTA 2016
CountryKorea, Republic of
CitySeoul
Period16/11/1716/11/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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  • Cite this

    Park, J., Hwang, S., Yang, J., Bae, K., Ko, H., & Kim, D. K. (2017). Distributed compressive sensing for correlated information sources. In J. J. Jung, & P. Kim (Eds.), Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings (pp. 130-137). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 194 LNICST). Springer Verlag. https://doi.org/10.1007/978-3-319-58967-1_15