Konnov, I.; Kashuba, A.; Laitinen, E. Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users. Math. Comput. Appl. 2018, 23, 31. https://doi.org/10.3390/mca23020031
Dual methods for optimal allocation of telecommunication network resources with several classes of users
|Author:||Konnov, Igor1,2; Kashuba, Aleksey2; Laitinen, Erkki3|
1Department of System Analysis and Information Technologies, Kazan Federal University, Kazan 420008, Russia
2Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kazan 420008, Russia
3Faculty of Science, University of Oulu, FI-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201901303476
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2019-01-30
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.
Mathematical and computational applications
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
I.K. and A.K. were supported by the RFBR grant, project No. 16-01-00109a. Also, I.K. and E.L. were supported by grants No. 315471 and No. 315366 from Academy of Finland.
|Academy of Finland Grant Number:||
315471 (Academy of Finland Funding decision)
315366 (Academy of Finland Funding decision)
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