Theoretical bound on modulation classification for multiple-input multiple-output (MIMO) systems over unknown, flat fading channels

Yu Liu, Alexander M. Haimovich, Wei Su, Emmanuel Kanterakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Likelihood-based algorithms identify the modulation of the transmitted signal based on the computation of the likelihood function of received signals under different hypotheses (modulation formats). An important class of likelihood-based algorithms for modulation classification problems first treats the unknown channels as deterministic, and replaces the channels by their estimates. In this paper, a novel theoretical bound on the performance of this class of algorithms is proposed for multiple-input multiple-output (MIMO) systems over unknown, flat fading channels. The performance bound is developed from the Cramer-Rao bound (CRB) of blind channel estimation. It provides a useful benchmark against which it is possible to compare the performance of modulation classification algorithms, and is tighter than the theoretical bound derived based on perfect channel knowledge.

Original languageEnglish (US)
Title of host publication2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479984282
DOIs
StatePublished - Apr 15 2015
Event2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 - Baltimore, United States
Duration: Mar 18 2015Mar 20 2015

Publication series

Name2015 49th Annual Conference on Information Sciences and Systems, CISS 2015

Other

Other2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
CountryUnited States
CityBaltimore
Period3/18/153/20/15

All Science Journal Classification (ASJC) codes

  • Information Systems

Keywords

  • Cramer-Rao bounds
  • MIMO
  • modulation classification
  • theoretical bound

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