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Continuum equilibria and global optimization for routing in dense static ad hoc networks
Authors:Alonso Silva  Eitan Altman  Pierre Bernhard  Mérouane Debbah
Affiliation:1. INRIA, B.P. 93, 2004 Route des Lucioles, 06902 Sophia-Antipolis Cedex, France;2. I3S, Université de Nice-Sophia Antipolis and CNRS, 940 Route des Colles, B.P. 145, 06903 Sophia-Antipolis Cedex, France;3. Alcatel-Lucent Chair in Flexible Radio – SUPELEC, 91192 Gif sur Yvette, France;1. Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence, College of Engineering, School of Electrical and Computer Engineering, University of Tehran, Iran;3. Department of Mathematics, University of Wisconsin-Eau Claire, Eau Claire, WI, USA;1. Department of Mathematics, College of Science, Sohag University, Sohag 82524, Egypt;2. Department of Mathematics, College of Science, Taif University, Taif 888, Saudi Arabia;3. Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
Abstract:We consider massively dense ad hoc networks and study their continuum limits as the node density increases and as the graph providing the available routes becomes a continuous area with location and congestion dependent costs. We study both the global optimal solution as well as the non-cooperative routing problem among a large population of users where each user seeks a path from its origin to its destination so as to minimize its individual cost. Finally, we seek for a (continuum version of the) Wardrop equilibrium. We first show how to derive meaningful cost models as a function of the scaling properties of the capacity of the network and of the density of nodes. We present various solution methodologies for the problem: (1) the viscosity solution of the Hamilton–Jacobi–Bellman equation, for the global optimization problem, (2) a method based on Green’s Theorem for the least cost problem of an individual, and (3) a solution of the Wardrop equilibrium problem using a transformation into an equivalent global optimization problem.
Keywords:
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