Treffer: Accelerated rare event simulation with Markov chain modelling in wireless communication networks
Title:
Accelerated rare event simulation with Markov chain modelling in wireless communication networks
Authors:
Source:
International journal of mobile network design and innovation (Print). 4(4):185-191
Publisher Information:
Genève: Inderscience Publishers, 2012.
Publication Year:
2012
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Télétrafic, Teletraffic, Transmission et modulation (techniques et équipements), Transmission and modulation (techniques and equipments), Gestion trafic, Traffic management, Gestión tráfico, Algorithme, Algorithm, Algoritmo, Chaîne Markov, Markov chain, Cadena Markov, Dépassement capacité, Overflow(computer arithmetics), Rebasamiento capacidad, Echantillonnage importance, Importance sampling, Entropie, Entropy, Entropía, Evénement rare, Rare event, Acontecimiento rara, Modélisation, Modeling, Modelización, Régulation trafic, Traffic control, Regulación tráfico, Réseau file attente, Queueing network, Red cola espera, Réseau sans fil, Wireless network, Red sin hilo, Réseau télécommunication, Telecommunication network, Red telecomunicación, Simulation numérique, Numerical simulation, Simulación numérica, Système attente, Queueing system, Sistema fila espera, Système tampon, Buffer system, Sistema amortiguador, Télécommunication sans fil, Wireless telecommunication, Telecomunicación sin hilo, Télétrafic, Teletraffic, Teletráfico, Markov chain modelling, accelerated rare event simulation, buffer overflow probability, change of measure, cross-entropy algorithm, importance sampling algorithm, queuing systems, rare event simulation, wireless telecommunications
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Management, Marketing and Information Systems, SUNY, Oneonta, NY 13820, United States
Information Sciences and Technology Penn State University, Berks, PA 19610, United States
Information Sciences and Technology Penn State University, Berks, PA 19610, United States
ISSN:
1744-2869
Rights:
Copyright 2014 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
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.27628122
Database:
PASCAL Archive
Weitere Informationen
The paper recommends an effective approach to estimate probability of buffer overflow in wireless communication networks. The buffer overflow probability in queuing systems is defined as a rare event and can be estimated using rare event simulation with Markov chains. Two-node queuing networks are considered in this paper; and an event of buffer overflow at the second node is studied. Two efficient rare event simulation algorithms, based on the importance sampling and cross-entropy methods, are developed and applied to accelerate the overflow probability simulation with Markov chain modelling. Numerical examples and simulation results are provided.