Non-asymptotic performance bounds of eigenvalue based detection of signals in non-Gaussian noise

Ron Heimann, Amir Leshem, Ephraim Zehavi, Anthony J. Weiss

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

Abstract

The core component of a cognitive radio is its detector. When a device is equipped with multiple antennas, the detection method is usually based on an eigenvalue analysis. This paper explores the performance of the most common largest eigenvalue detector, for the case of a narrowband temporally white signal and calibrated receiver noise. In contrast to popular Gaussian assumption, our performance bounds are valid for any signal and noise that belong to the wide class of sub-Gaussian random processes. Moreover, the results are given in closed-form for any finite number of observations and antennas, in contrary to the widespread asymptotic analysis approach.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2936-2940
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - 18 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Chernoff bound
  • Sensor array
  • cognitive radio
  • random matrix
  • sub-Gaussian random variables

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