A qHLRT modulation classifier with antenna array and two-stage CFO estimation in fading channels

Hong Li, Yeheskel Bar-Ness, Ali Abdi, Wei Su

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

2 Scopus citations


A likelihood ratio test (LRT) -based modulation classifier is sensitive to unknown parameters, such as carrier frequency offset (CFO), phase shift, etc. To better handle this problem, a robust antenna array -based quasi-hybrid likelihood ratio test (qHLRT) approach is proposed in this paper. A nonmaximum likelihood (ML) estimator is employed to reduce the computational burden of multivariate maximization. A double CFO estimation scheme is also proposed, which increases the accuracy of CFO estimation. To deal with channel fading, maximal ratio combining approach is applied for CFO estimation as well as the computation of the likelihood functions. It is shown that when implementing with the nonlinear least-squares (NLS) phase parameters estimator and the method-of-moment (MoM) amplitude estimator, our scheme offers an effective way to recognize linear modulation formats with unknown parameters in fading channels.

Original languageEnglish (US)
Title of host publication2006 IEEE Sarnoff Symposium
StatePublished - 2006
Event2006 IEEE Sarnoff Symposium - Princeton, NJ, United States
Duration: Mar 27 2006Mar 28 2006

Publication series

Name2006 IEEE Sarnoff Symposium


Other2006 IEEE Sarnoff Symposium
Country/TerritoryUnited States
CityPrinceton, NJ

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication


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