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Non-Data-Aided Symbol RateEstimation of Linearly Modulated Signals

Mosquera, Carlos and Scalise, Sandro and Lopez Valcarce, Roberto (2006) Non-Data-Aided Symbol RateEstimation of Linearly Modulated Signals. IEEE Transactions on Signal Processing, pp. 664-674. IEEE. DOI: 10.1109/TSP.2007.907888 ISSN 1053-587X

Full text not available from this repository.

Official URL: http://ewh.ieee.org/soc/sps/tsp/

Abstract

The estimation of the symbol rate of a linearly modulated signal is addressed, with special focus on low signal-to-noise ratio (SNR) scenarios. This problem finds application in automatic modulation classification and signal monitoring. A maximum-likelihood (ML) approach is adopted to derive practical estimators, exploiting information on the cyclostationarity of the modulated signal as well as knowledge of the received signaling pulse shape. The structure of the ML estimator suggests a two-step estimation procedure, whereby an initial coarse search determines first a neighborhood from which a subsequent fine search yields the final symbol rate estimate. Links between the ML approach and previous results from the literature in symbol rate estimation are established as well. The proposed scheme is applicable even for small excess bandwidths, at the cost of a higher complexity with respect to simpler estimators known to fail under such conditions.

Item URL in elib:https://elib.dlr.de/48988/
Document Type:Article
Title:Non-Data-Aided Symbol RateEstimation of Linearly Modulated Signals
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mosquera, CarlosUniversity of VigoUNSPECIFIED
Scalise, SandroUNSPECIFIEDUNSPECIFIED
Lopez Valcarce, RobertoUniversity of VigoUNSPECIFIED
Date:December 2006
Journal or Publication Title:IEEE Transactions on Signal Processing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
DOI :10.1109/TSP.2007.907888
Page Range:pp. 664-674
Publisher:IEEE
ISSN:1053-587X
Status:Published
Keywords:Cyclostationarity, frequency estimation, maximum- likelihood (ML) estimation, non-data-aided, synchronization.
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W - no assignment
DLR - Research area:Space
DLR - Program:W - no assignment
DLR - Research theme (Project):W - no assignment (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Digital Networks
Deposited By: Scalise, Dr.-Ing. Sandro
Deposited On:21 Jan 2008
Last Modified:15 Jan 2010 00:54

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