Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users

We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ require...

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Published in:Mathematical and Computational Applications
Main Authors: Igor Konnov, Aleksey Kashuba, Erkki Laitinen
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2018
Subjects:
DML
Online Access:https://doi.org/10.3390/mca23020031
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spelling ftmdpi:oai:mdpi.com:/2297-8747/23/2/31/ 2023-08-20T04:06:10+02:00 Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users Igor Konnov Aleksey Kashuba Erkki Laitinen 2018-06-17 application/pdf https://doi.org/10.3390/mca23020031 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/mca23020031 https://creativecommons.org/licenses/by/4.0/ Mathematical and Computational Applications; Volume 23; Issue 2; Pages: 31 telecommunication networks wireless networks service levels resource allocation optimization problem decomposition methods Lagrange duality Text 2018 ftmdpi https://doi.org/10.3390/mca23020031 2023-07-31T21:34:53Z We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution to this problem, which exploit its special features. We suggest applying first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solving several optimization problems. Our methods differ in approaches for solving these auxiliary problems. We consider three basic methods: Dual Multi Layer (DML), Conditional Gradient Dual Multilayer (CGDM) and Bisection (BS). Besides these methods we consider their modifications adjusted to different kind of cost functions. Our comparison of the performance of the suggested methods on several series of test problems show satisfactory convergence. Nevertheless, proper decomposition techniques enhance the convergence essentially. Text DML MDPI Open Access Publishing Lagrange ENVELOPE(-62.597,-62.597,-64.529,-64.529) Mathematical and Computational Applications 23 2 31
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic telecommunication networks
wireless networks
service levels
resource allocation
optimization problem
decomposition methods
Lagrange duality
spellingShingle telecommunication networks
wireless networks
service levels
resource allocation
optimization problem
decomposition methods
Lagrange duality
Igor Konnov
Aleksey Kashuba
Erkki Laitinen
Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
topic_facet telecommunication networks
wireless networks
service levels
resource allocation
optimization problem
decomposition methods
Lagrange duality
description We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution to this problem, which exploit its special features. We suggest applying first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solving several optimization problems. Our methods differ in approaches for solving these auxiliary problems. We consider three basic methods: Dual Multi Layer (DML), Conditional Gradient Dual Multilayer (CGDM) and Bisection (BS). Besides these methods we consider their modifications adjusted to different kind of cost functions. Our comparison of the performance of the suggested methods on several series of test problems show satisfactory convergence. Nevertheless, proper decomposition techniques enhance the convergence essentially.
format Text
author Igor Konnov
Aleksey Kashuba
Erkki Laitinen
author_facet Igor Konnov
Aleksey Kashuba
Erkki Laitinen
author_sort Igor Konnov
title Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
title_short Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
title_full Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
title_fullStr Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
title_full_unstemmed Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
title_sort dual methods for optimal allocation of telecommunication network resources with several classes of users
publisher Multidisciplinary Digital Publishing Institute
publishDate 2018
url https://doi.org/10.3390/mca23020031
long_lat ENVELOPE(-62.597,-62.597,-64.529,-64.529)
geographic Lagrange
geographic_facet Lagrange
genre DML
genre_facet DML
op_source Mathematical and Computational Applications; Volume 23; Issue 2; Pages: 31
op_relation https://dx.doi.org/10.3390/mca23020031
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/mca23020031
container_title Mathematical and Computational Applications
container_volume 23
container_issue 2
container_start_page 31
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