Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Distributed largest eigenvalue detection
KTH, School of Electrical Engineering (EES), Information Science and Engineering. Tallinn University of Technology, Estonia.
KTH, School of Electrical Engineering (EES), Information Science and Engineering.
2017 (English)In: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3519-3523, article id 7952811Conference paper (Refereed)
Abstract [en]

Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. In this paper we study distributed spectrum sensing, based on the largest eigenvalue of adaptively estimated correlation matrices (CMs) of received signals. The PU signal is assumed to be temporally correlated. In this paper an Combine and Adapt (CTA) least Mean Square (LMS) diffusion based mean vector estimation scheme is proposed. No fusion center (FC) for estimation or detection is used. We analyse the resulting detection performance and verify the theoretical findings through simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 3519-3523, article id 7952811
Series
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149
Keywords [en]
Cognitive radio, diffusion LMS, distributed detection, distributed estimation, Spectrum Sensing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-213255DOI: 10.1109/ICASSP.2017.7952811ISI: 000414286203137Scopus ID: 2-s2.0-85023768307ISBN: 9781509041176 OAI: oai:DiVA.org:kth-213255DiVA, id: diva2:1137047
Conference
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, Hilton New Orleans RiversideNew Orleans, United States, 5 March 2017 through 9 March 2017
Note

QC 20170830

Available from: 2017-08-30 Created: 2017-08-30 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ainomäe, AhtiBengtsson, Mats
By organisation
Information Science and Engineering
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 54 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf