• Performance evaluation of the...

Treffer: Performance evaluation of the correntropy coefficient in automatic modulation classification

Title:
Performance evaluation of the correntropy coefficient in automatic modulation classification
Authors:
FONTES, Aluisio I. R, MARTINS, Allan de M, SILVEIRA, Luiz F. Q, PRINCIPE, J. C
Source:
Expert systems with applications. 42(1):1-8
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, 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, Radiocommunications, Equipements et installations, Equipments and installations, Radiocommunications du service mobile, Mobile radiocommunication systems, Classification automatique, Automatic classification, Clasificación automática, Détection signal, Signal detection, Detección señal, Entropie, Entropy, Entropía, Evaluation performance, Performance evaluation, Evaluación prestación, Extensibilité, Scalability, Estensibilidad, Mesure information, Information measure, Medida información, Modulation adaptative, Adaptive modulation, Modulación adaptativa, Moment statistique, Statistical moment, Momento estadístico, Monitorage, Monitoring, Monitoreo, Méthode noyau, Kernel method, Método núcleo, Métrique, Metric, Métrico, Précision élevée, High precision, Precisión elevada, Radio logicielle, Software radio, Radio logicial, Rapport signal bruit, Signal to noise ratio, Relación señal ruido, Reconnaissance automatique, Automatic recognition, Reconocimiento automático, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Réseau sans fil, Wireless network, Red sin hilo, Similitude, Similarity, Similitud, Surveillance, Vigilancia, Système adaptatif, Adaptive system, Sistema adaptativo, Théorie information, Information theory, Teoría información, Théorie mesure, Measure theory, Teoría medida, Automatic modulation classification, Correntropy coefficient
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, 59078-900 Natal, Brazil
Computational Neuroengineering Laboratory (CNEL), Department of ECE, University of Florida, Gainesville, FL 32611, United States
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843378
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:
Computer science; theoretical automation; systems

Telecommunications and information theory
Accession Number:
edscal.28843378
Database:
PASCAL Archive

Weitere Informationen

Automatic modulation classification (AMC) techniques have applications in a variety of wireless communication scenarios, such as adaptive systems, cognitive radio, and surveillance systems. However, a common requirement to most of the AMC techniques proposed in the literature is the use of signal preprocessing modules, which can increase the computational cost and decrease the scalability of the AMC strategy. This work proposes the direct use of a similarity measure based on information theory for the automatic recognition of digital modulations, which is known as correntropy coefficient. The performance of correntropy in AMC applied to channels subject to additive white Gaussian noise (AWGN) is evaluated. Specifically, the influence of the kernel size on the classifier performance is analyzed, since it is the only free parameter in correntropy. Besides, a relationship between its respective value and the signal-to-noise ratio (SNR) of the channel is also proposed. Considering the investigated modulation techniques, numerical results obtained by simulation demonstrate that there are high accuracy rates in classification, even at low SNR values. By using correntropy, AMC task becomes simpler and more efficient.