Carrier frequency offset estimation in qHLRT modulation classifier with antenna arrays

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

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

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2006 IEEE Wireless Communications and Networking Conference, WCNC 2006
Pages1465-1470
Number of pages6
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE Wireless Communications and Networking Conference, WCNC 2006 - Las Vegas, NV, United States
Duration: Apr 3 2006Apr 6 2006

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume3
ISSN (Print)1525-3511

Other

Other2006 IEEE Wireless Communications and Networking Conference, WCNC 2006
CountryUnited States
CityLas Vegas, NV
Period4/3/064/6/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Carrier frequency offset estimation in qHLRT modulation classifier with antenna arrays'. Together they form a unique fingerprint.

Cite this