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Distributed Detection in Cognitive Radio Networks
KTH, School of Electrical Engineering (EES), Information Science and Engineering.ORCID iD: 0000-0001-9814-2944
2017 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized.

In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied.

In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations.

In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. , p. 109
Series
TRITA-EE, ISSN 1653-5146 ; 109
Keywords [en]
Cognitive Radio, distributed estimation, distributed detection, Diffusion LMS, Diffusion Networks, Adaptive Networks, Spectrum Sensing, Energy Detection, Random Matrix, Largest Eigenvalue Detection.
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-213957ISBN: 978-91-7729-515-0 (print)OAI: oai:DiVA.org:kth-213957DiVA, id: diva2:1139346
Presentation
2017-09-28, V3, Teknikringen 72, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20170908

Available from: 2017-09-08 Created: 2017-09-07 Last updated: 2017-09-08Bibliographically approved

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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