Carrier frequency offset estimation in qHLRT modulation classifier with antenna arrays

Document Type

Conference Proceeding

Publication Date

12-1-2006

Abstract

A likelihood ratio test (LRT) -based modulation classifier is sensitive to unknown parameters, such as carrier frequency offset (CFO), symbol rate, etc. To deal with the limited knowledge of CFO, in this paper, a quasi-hybrid likelihood ratio test (qHLRT) -based approach is proposed for linear modulation classification. In the qHLRT algorithm, a non-maximum likelihood (ML) estimator is used to reduce the computational burden of multivariate maximization. Several of blind, non-ML CFO estimators are studied and their performance are compared with both single and multiple receiving antennas systems. It is shown that the nonlinear least-squares (NLS) CFO estimator is the best choice for the qHLRT algorithm, particularly with antenna arrays, which are introduced to combat the effect of channel fading on modulation classification. © 2006 IEEE.

Identifier

34250333085 (Scopus)

ISBN

[0780377001, 1424402700, 9781424402700]

Publication Title

IEEE Wireless Communications and Networking Conference Wcnc

External Full Text Location

https://doi.org/10.1109/WCNC.2003.1200602

ISSN

15253511

First Page

1465

Last Page

1470

Volume

3

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