Utilization of partial common information in distributed compressive sensing

Jeong Hun Park; Seung Gye Hwang; Dong Ku Kim; Jang Hoon Yang

doi:10.1109/VETECS.2012.6240176

Utilization of partial common information in distributed compressive sensing

Jeong Hun Park, Seung Gye Hwang, Dong Ku Kim, Jang Hoon Yang

Division of Engineering and Science (Abington)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In a densely distributed sensor network, it is known that it is advantageous to exploit both intra- and inter- signal correlation structures during recovery procedure. Based on this observation, the bound of the required number of measurement in distributed compressive sensing (DCS) is shown to be reduced by exploiting this structure[2]. In this paper, we generalize the model for distributed compressive sensing and show that elaborated signal structure can reduce the required number of measurements further. To this end, we introduce a new concept of partial common information which is shared by some parts of signals, but not by every signal. Numerical results show that with the proposed model, more robust signal recovery can be achieved.

Original language	English (US)
Title of host publication	IEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Proceedings
DOIs	https://doi.org/10.1109/VETECS.2012.6240176
State	Published - 2012
Event	IEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Yokohama, Japan Duration: May 6 2012 → Jun 9 2012

Publication series

Name	IEEE Vehicular Technology Conference
ISSN (Print)	1550-2252

Other

Other	IEEE 75th Vehicular Technology Conference, VTC Spring 2012
Country/Territory	Japan
City	Yokohama
Period	5/6/12 → 6/9/12

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/VETECS.2012.6240176

Cite this

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title = "Utilization of partial common information in distributed compressive sensing",

abstract = "In a densely distributed sensor network, it is known that it is advantageous to exploit both intra- and inter- signal correlation structures during recovery procedure. Based on this observation, the bound of the required number of measurement in distributed compressive sensing (DCS) is shown to be reduced by exploiting this structure[2]. In this paper, we generalize the model for distributed compressive sensing and show that elaborated signal structure can reduce the required number of measurements further. To this end, we introduce a new concept of partial common information which is shared by some parts of signals, but not by every signal. Numerical results show that with the proposed model, more robust signal recovery can be achieved.",

author = "Park, {Jeong Hun} and Hwang, {Seung Gye} and Kim, {Dong Ku} and Yang, {Jang Hoon}",

year = "2012",

doi = "10.1109/VETECS.2012.6240176",

language = "English (US)",

isbn = "9781467309905",

series = "IEEE Vehicular Technology Conference",

booktitle = "IEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Proceedings",

note = "IEEE 75th Vehicular Technology Conference, VTC Spring 2012 ; Conference date: 06-05-2012 Through 09-06-2012",

}

Park, JH, Hwang, SG, Kim, DK & Yang, JH 2012, Utilization of partial common information in distributed compressive sensing. in IEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Proceedings., 6240176, IEEE Vehicular Technology Conference, IEEE 75th Vehicular Technology Conference, VTC Spring 2012, Yokohama, Japan, 5/6/12. https://doi.org/10.1109/VETECS.2012.6240176

TY - GEN

T1 - Utilization of partial common information in distributed compressive sensing

AU - Park, Jeong Hun

AU - Hwang, Seung Gye

AU - Kim, Dong Ku

AU - Yang, Jang Hoon

PY - 2012

Y1 - 2012

N2 - In a densely distributed sensor network, it is known that it is advantageous to exploit both intra- and inter- signal correlation structures during recovery procedure. Based on this observation, the bound of the required number of measurement in distributed compressive sensing (DCS) is shown to be reduced by exploiting this structure[2]. In this paper, we generalize the model for distributed compressive sensing and show that elaborated signal structure can reduce the required number of measurements further. To this end, we introduce a new concept of partial common information which is shared by some parts of signals, but not by every signal. Numerical results show that with the proposed model, more robust signal recovery can be achieved.

AB - In a densely distributed sensor network, it is known that it is advantageous to exploit both intra- and inter- signal correlation structures during recovery procedure. Based on this observation, the bound of the required number of measurement in distributed compressive sensing (DCS) is shown to be reduced by exploiting this structure[2]. In this paper, we generalize the model for distributed compressive sensing and show that elaborated signal structure can reduce the required number of measurements further. To this end, we introduce a new concept of partial common information which is shared by some parts of signals, but not by every signal. Numerical results show that with the proposed model, more robust signal recovery can be achieved.

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U2 - 10.1109/VETECS.2012.6240176

DO - 10.1109/VETECS.2012.6240176

M3 - Conference contribution

AN - SCOPUS:84864974244

SN - 9781467309905

T3 - IEEE Vehicular Technology Conference

BT - IEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Proceedings

T2 - IEEE 75th Vehicular Technology Conference, VTC Spring 2012

Y2 - 6 May 2012 through 9 June 2012

ER -

Utilization of partial common information in distributed compressive sensing

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