Non-asymptotic analysis of scaled largest eigenvalue based spectrum sensing

Lu Wei*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In this paper, we analyze the non-asymptotic performance of scaled largest eigenvalue based detection, which is an optimal detector in the presence of a single primary user. Exact distributions of the test statistics have been derived, which lead to finite-dimensional characterizations of the false alarm probability. These results are obtained by taking advantage of the properties of the Mellin transform for products of independent random variables. Simulations are provided to verify the derived results, and to compare with the asymptotic result in literature.

Original languageEnglish
Title of host publication2013 IEEE 77th Vehicular Technology Conference, VTC Spring 2013 - Proceedings
DOIs
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventIEEE Vehicular Technology Conference - Dresden, Germany
Duration: 2 Jun 20135 Jun 2013
Conference number: 77

Publication series

NameIEEE Vehicular Technology Conference Proceedings
ISSN (Print)1550-2252

Conference

ConferenceIEEE Vehicular Technology Conference
Abbreviated titleVTC Spring
CountryGermany
CityDresden
Period02/06/201305/06/2013

Keywords

  • Cognitive radio
  • Multi-antenna spectrum sensing
  • Multivariate analysis
  • The Mellin transform

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