%0 Conference Proceedings
%T Addressing Phase Transitions in Wireless Networking Optimization
%+ Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH)
%A Michalopoulou, Maria
%A Mähönen, Petri
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 7th International Workshop on Self-Organizing Systems (IWSOS)
%C Palma de Mallorca, Spain
%Y David Hutchison
%Y Takeo Kanade
%Y Madhu Sudan
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Moshe Y. Vardi
%Y Gerhard Weikum
%Y Wilfried Elmenreich
%Y Falko Dressler
%Y Vittorio Loreto
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%Y Bernhard Steffen
%I Springer
%3 Self-Organizing Systems
%V LNCS-8221
%P 103-114
%8 2013-05-09
%D 2013
%R 10.1007/978-3-642-54140-7_9
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X The general aim of this paper is to introduce the notion of phase transitions into wireless networking optimization. Although the theory of phase transitions from statistical physics has been employed in optimization theory, phase transitions in the context of optimization of wireless networks have not yet been considered. In wireless networking optimization, given one or more optimization objectives we often need to define mathematically an optimization task, so that a set of requirements is not violated. However, especially recent trends in wireless communications, such as self-organized networks, femto-cellular systems, and cognitive radios, calls for optimization approaches that can be implemented in a distributed and decentralized fashion. Thus we are interested to find utility-based approaches that can be practically employed in a self-organizing network. We argue that phase transitions can be identified and taken appropriately into account in order to eliminate the emergence of undesirable solutions that lie near the point where the phase transition occurs. As an example we present a simple power control problem for a macrocell-femtocell network scenario. We formulate a distributed framework of the problem where we model a phase transition effect by means of a dummy variable in order to exclude solutions lying in the one side of the phase transition.
%G English
%Z TC 6
%2 https://inria.hal.science/hal-01291504/document
%2 https://inria.hal.science/hal-01291504/file/978-3-642-54140-7_9_Chapter.pdf
%L hal-01291504
%U https://inria.hal.science/hal-01291504
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC6
%~ IFIP-LNCS-8221