Result: On the Likelihood-Based Approach to Modulation Classification

Title:
On the Likelihood-Based Approach to Modulation Classification
Authors:
HAMEED, Fahed, DOBRE, Octavia A, POPESCU, Dimitrie C
Source:
IEEE transactions on wireless communications. 8(12):5884-5892
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2009.
Publication Year:
2009
Physical Description:
print, 23 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Modulation, démodulation, Modulation, demodulation, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Transmission et modulation (techniques et équipements), Transmission and modulation (techniques and equipments), Algorithme, Algorithm, Algoritmo, Articulation, Joint, Articulación, Borne supérieure, Upper bound, Cota superior, Bruit phase, Phase noise, Ruido fase, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Cumulant, Cumulante, Estimation paramètre, Parameter estimation, Estimación parámetro, Etude cas, Case study, Estudio caso, Evaluation performance, Performance evaluation, Evaluación prestación, Fonction logarithmique, Logarithmic function, Función logarítmica, Inégalité Cramer Rao, Cramer Rao inequality, Desigualdad Cramer Rao, Modulation binaire déplacement phase, Binary phase shift keying, Modulación desplazamiento fase bivalente, Modulation déplacement phase en quadrature, Quadrature phase shift keying, Modulation linéaire, Linear modulation, Modulación lineal, Méthode moment, Moment method, Método momento, Rapport vraisemblance, Likelihood ratio, Relación verosimilitud, Test rapport vraisemblance, Likelihood ratio test, Test razón verosimilitud, Cramer-Rao lower bounds, joint parameter estimation, likelihood ratio test, modulation classification
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 300 Prince Phillip Dr., St. John's, NL, A1B 3X5, Canada
Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA 23529, United States
ISSN:
1536-1276
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=22308964
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.22308964
Database:
PASCAL Archive

Further Information

In this paper, likelihood-based algorithms are explored for linear digital modulation classification. Hybrid Likelihood Ratio Test (HLRT)- and Quasi HLRT (QHLRT)- based algorithms are examined, with signal amplitude, phase, and noise power as unknown parameters. The algorithm complexity is first investigated, and findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution. An upper bound on the performance of QHLRT-based algorithms, which employ unbiased and normally-distributed non-data aided estimates of the unknown parameters, is proposed. This is referred to as the QHLRT-Upper Bound (QHLRT-UB). Classification of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals is presented as a case study. The Cramer-Rao Lower Bounds (CRBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for BPSK and QPSK signals, and further employed to obtain the QHLRT-UB. An upper bound on classification performance of any likelihood-based algorithms is also introduced. Method-of-moments (MoM) estimates of the unknown parameters are investigated and used to develop the QHLRT-based algorithm. Classification performance of this algorithm is compared with the upper bounds, as well as with the quasi Log-Likelihood Ratio (qLLR) and fourth-order cumulant based algorithms.