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’ 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.

[1]  John Wiley,et al.  Pricing Communication Networks : Economics, Technology and Modeling , 2014 .

[2]  I. Konnov,et al.  Application of the conditional gradient method to resource allocation in wireless networks , 2016, Lobachevskii Journal of Mathematics.

[4]  Omar Raoof,et al.  Auction and Game-based Spectrum Sharing in Cognitive Radio Networks , 2010 .

[5]  A. Leshem,et al.  Game theory and the frequency selective interference channel , 2009, IEEE Signal Processing Magazine.

[6]  I. Konnov Modelling of Auction Type Markets , 2007 .

[7]  I. Konnov,et al.  Decomposition method for zonal resource allocation problems in telecommunication networks , 2016 .

[8]  Igor Konnov,et al.  TWO-LEVEL DECOMPOSITION METHOD FOR RESOURCEALLOCATION IN TELECOMMUNICATION NETWORKS , 2012 .

[9]  B. N. Pshenichnyi,et al.  Numerical Methods in Extremal Problems. , 1978 .

[10]  Slawomir Stanczak,et al.  Resource allocation in wireless networks , 2006 .

[11]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[12]  Erkki Laitinen,et al.  Dual iterative methods for nonlinear total resource allocation problems in telecommunication networks , 2017 .

[13]  Erkki Laitinen,et al.  Optimisation problems for control of distributed resources , 2011, Int. J. Model. Identif. Control..

[14]  Costas Courcoubetis,et al.  Pricing communication networks - economics, technology and modelling , 2003, Wiley-Interscience series in systems and optimization.

[15]  Enzo Baccarelli,et al.  Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees , 2015, Veh. Commun..

[16]  Y. Thomas Hou,et al.  Cognitive radio communications and networks: principles and practice , 2012 .

[17]  Slawomir Stanczak,et al.  Resource Allocation in Wireless Networks: Theory and Algorithms , 2006, Lecture Notes in Computer Science.

[18]  George Iosifidis,et al.  Auction mechanisms for network resource allocation , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[19]  Enzo Baccarelli,et al.  Energy-saving self-configuring networked data centers , 2013, Comput. Networks.

[20]  Michael Patriksson,et al.  Algorithms for the continuous nonlinear resource allocation problem - New implementations and numerical studies , 2015, Eur. J. Oper. Res..

[21]  Michael L. Honig,et al.  Auction-Based Spectrum Sharing , 2006, Mob. Networks Appl..