Quasi-hybrid likelihood modulation classification with nonlinear carrier frequency offsets estimation using antenna arrays

Document Type

Conference Proceeding

Publication Date

1-1-2005

Abstract

A quasi-hybrid likelihood ratio test (qHLRT) -based algorithm is proposed for linear modulation classification, with unknown carrier frequency offsets (CFO). A blind symbol-ratesampling nonlinear least-squares (NLS) CFO estimator, which has the advantage of simplicity relative to other estimator that requires over-sampling, is incorporated in the qHLRT algorithm. The classifier is simple to implement yet provides accurate enough classification. A receive antenna array is added to further enhance the classification performance. Although the method can be applied to different linear digital modulation, we concentrate in this paper on M-ary QAM. Simulation results presented for classification of M-ary QAM signals under AWGN channel show that the qHLRT classifier with NLS estimator offers an efficient way to combat the sensitivity to unknown CFO of the average likelihood ratio test (ALRT) algorithm, and the performance can be further improved by spatial diversity.

Identifier

33847363562 (Scopus)

ISBN

[0780393937, 9780780393936]

Publication Title

Proceedings IEEE Military Communications Conference MILCOM

External Full Text Location

https://doi.org/10.1109/MILCOM.2005.1605743

First Page

570

Last Page

575

Volume

2005

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