Distributed compressive wide-band spectrum sensing

Ying Wang; Ashish Pandharipande; Yvan Lamelas Polo; Geert Leus

doi:10.1109/ITA.2009.5044942

Distributed compressive wide-band spectrum sensing

Ying Wang, Ashish Pandharipande, Yvan Lamelas Polo, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

87 Citations (Scopus)

Abstract

We consider a compressive wide-band spectrum sensing scheme for cognitive radio networks. Each cognitive radio (CR) sensing receiver transforms the received analog signal from the licensed system in to a digital signal using an analog-toinformation converter. The autocorrelation of the compressed signal is then collected from each CR at a fusion center. A compressive sampling recovery algorithm that exploits joint sparsity is then employed to reconstruct an estimate of the signal spectrum and used to make a decision on signal occupancy. We compare the performance of this distributed compressive spectrum sensing scheme with a compressive spectrum sensing scheme at a single CR and show the performance gains obtained from spatial diversity.

Original language	English
Title of host publication	Information Theory and Applications Workshop, ITA 2009
Publisher	Institute of Electrical and Electronics Engineers
Pages	178-183
Number of pages	6
ISBN (Print)	9781424439904
DOIs	https://doi.org/10.1109/ITA.2009.5044942
Publication status	Published - 2009
Externally published	Yes
Event	Information Theory and Applications Workshop, ITA 2009 - San Diego, CA, United States Duration: 8 Feb 2009 → 13 Feb 2009

Conference

Conference	Information Theory and Applications Workshop, ITA 2009
Country/Territory	United States
City	San Diego, CA
Period	8/02/09 → 13/02/09

Keywords

Cognitive radio
Distributed compressive sampling
Spectrum estimation
Wide-band spectrum sensing

Access to Document

10.1109/ITA.2009.5044942

Cite this

@inproceedings{bcaf8ee5453142619e07f1abc7e8d5a6,

title = "Distributed compressive wide-band spectrum sensing",

abstract = "We consider a compressive wide-band spectrum sensing scheme for cognitive radio networks. Each cognitive radio (CR) sensing receiver transforms the received analog signal from the licensed system in to a digital signal using an analog-toinformation converter. The autocorrelation of the compressed signal is then collected from each CR at a fusion center. A compressive sampling recovery algorithm that exploits joint sparsity is then employed to reconstruct an estimate of the signal spectrum and used to make a decision on signal occupancy. We compare the performance of this distributed compressive spectrum sensing scheme with a compressive spectrum sensing scheme at a single CR and show the performance gains obtained from spatial diversity.",

keywords = "Cognitive radio, Distributed compressive sampling, Spectrum estimation, Wide-band spectrum sensing",

author = "Ying Wang and Ashish Pandharipande and Polo, {Yvan Lamelas} and Geert Leus",

year = "2009",

doi = "10.1109/ITA.2009.5044942",

language = "English",

isbn = "9781424439904",

pages = "178--183",

booktitle = "Information Theory and Applications Workshop, ITA 2009",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "Information Theory and Applications Workshop, ITA 2009 ; Conference date: 08-02-2009 Through 13-02-2009",

}

Wang, Y, Pandharipande, A, Polo, YL & Leus, G 2009, Distributed compressive wide-band spectrum sensing. in Information Theory and Applications Workshop, ITA 2009., 5044942, Institute of Electrical and Electronics Engineers, pp. 178-183, Information Theory and Applications Workshop, ITA 2009, San Diego, CA, United States, 8/02/09. https://doi.org/10.1109/ITA.2009.5044942

TY - GEN

T1 - Distributed compressive wide-band spectrum sensing

AU - Wang, Ying

AU - Pandharipande, Ashish

AU - Polo, Yvan Lamelas

AU - Leus, Geert

PY - 2009

Y1 - 2009

N2 - We consider a compressive wide-band spectrum sensing scheme for cognitive radio networks. Each cognitive radio (CR) sensing receiver transforms the received analog signal from the licensed system in to a digital signal using an analog-toinformation converter. The autocorrelation of the compressed signal is then collected from each CR at a fusion center. A compressive sampling recovery algorithm that exploits joint sparsity is then employed to reconstruct an estimate of the signal spectrum and used to make a decision on signal occupancy. We compare the performance of this distributed compressive spectrum sensing scheme with a compressive spectrum sensing scheme at a single CR and show the performance gains obtained from spatial diversity.

AB - We consider a compressive wide-band spectrum sensing scheme for cognitive radio networks. Each cognitive radio (CR) sensing receiver transforms the received analog signal from the licensed system in to a digital signal using an analog-toinformation converter. The autocorrelation of the compressed signal is then collected from each CR at a fusion center. A compressive sampling recovery algorithm that exploits joint sparsity is then employed to reconstruct an estimate of the signal spectrum and used to make a decision on signal occupancy. We compare the performance of this distributed compressive spectrum sensing scheme with a compressive spectrum sensing scheme at a single CR and show the performance gains obtained from spatial diversity.

KW - Cognitive radio

KW - Distributed compressive sampling

KW - Spectrum estimation

KW - Wide-band spectrum sensing

UR - http://www.scopus.com/inward/record.url?scp=70349284998&partnerID=8YFLogxK

U2 - 10.1109/ITA.2009.5044942

DO - 10.1109/ITA.2009.5044942

M3 - Conference contribution

AN - SCOPUS:70349284998

SN - 9781424439904

SP - 178

EP - 183

BT - Information Theory and Applications Workshop, ITA 2009

PB - Institute of Electrical and Electronics Engineers

T2 - Information Theory and Applications Workshop, ITA 2009

Y2 - 8 February 2009 through 13 February 2009

ER -

Distributed compressive wide-band spectrum sensing

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this