Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

Ivan Marsa-Maestre (), Enrique Hoz (), Jose Manuel Gimenez-Guzman (), David Orden and Mark Klein ()
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Ivan Marsa-Maestre: University of Alcalá
Enrique Hoz: University of Alcalá
Jose Manuel Gimenez-Guzman: University of Alcalá
Mark Klein: Center for Collective Intelligence, MIT

Group Decision and Negotiation, 2019, vol. 28, issue 1, No 8, 175-196

Abstract: Abstract At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climbing mediated negotiation and a simulated annealing mediated negotiation. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Moreover, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer. Finally, we show how the different strategic behavior of the participants affects the results.

Date: 2019
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DOI: 10.1007/s10726-018-9600-z

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