Array-based linear modulation classifier with two-stage CFO estimation in fading channels

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

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

Abstract

Likelihood ratio test (LRT) -based linear modulation classifier is sensitive to unknown parameters, such as carrier frequency offset (CFO), phase shift, etc. An antenna array-based quasi-hybrid likelihood ratio test (qHLRT) approach is proposed to cope with the problem. A non-maximum likelihood (ML) estimator is employed to reduce the computational burden of multivariate maximization. A two-stage CFO estimation scheme is also proposed to increase the accuracy of CFO estimation. To combat channel fading, maximal ratio combining (MRC) technique is applied for CFO estimation as well as the computation of the likelihood functions. The Cramer-Rao lower bound (CRLB) of the proposed CFO estimation method is derived. It is shown that with nonlinear least-squares (NLS) algorithm and method-of-moment (MoM) algorithm to estimate phase and amplitude respectively, our scheme offers an effective and practical solution to recognize linear modulation formats in fading channels.

Original languageEnglish (US)
Title of host publicationIEEE GLOBECOM 2006 - 2006 Global Telecommunications Conference
DOIs
StatePublished - Dec 1 2006
EventIEEE GLOBECOM 2006 - 2006 Global Telecommunications Conference - San Francisco, CA, United States
Duration: Nov 27 2006Dec 1 2006

Other

OtherIEEE GLOBECOM 2006 - 2006 Global Telecommunications Conference
Country/TerritoryUnited States
CitySan Francisco, CA
Period11/27/0612/1/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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