Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment
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 mo...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Netherlands
2021
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| Online Access: | https://hdl.handle.net/1721.1/131775 |
| _version_ | 1826209611496030208 |
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| author | Marsa-Maestre, Ivan de la Hoz, Enrique Gimenez-Guzman, Jose M Orden, David Klein, Mark |
| author2 | Massachusetts Institute of Technology. Center for Collective Intelligence |
| author_facet | Massachusetts Institute of Technology. Center for Collective Intelligence Marsa-Maestre, Ivan de la Hoz, Enrique Gimenez-Guzman, Jose M Orden, David Klein, Mark |
| author_sort | Marsa-Maestre, Ivan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:25:32Z |
| format | Article |
| id | mit-1721.1/131775 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:25:32Z |
| publishDate | 2021 |
| publisher | Springer Netherlands |
| record_format | dspace |
| spelling | mit-1721.1/1317752023-09-26T19:44:53Z Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment Marsa-Maestre, Ivan de la Hoz, Enrique Gimenez-Guzman, Jose M Orden, David Klein, Mark Massachusetts Institute of Technology. Center for Collective Intelligence 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. 2021-09-20T17:30:13Z 2021-09-20T17:30:13Z 2018-11-22 2020-09-24T20:37:40Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131775 en https://doi.org/10.1007/s10726-018-9600-z Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Nature B.V. application/pdf Springer Netherlands Springer Netherlands |
| spellingShingle | Marsa-Maestre, Ivan de la Hoz, Enrique Gimenez-Guzman, Jose M Orden, David Klein, Mark Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title_full | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title_fullStr | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title_full_unstemmed | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title_short | Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment |
| title_sort | nonlinear negotiation approaches for complex network optimization a study inspired by wi fi channel assignment |
| url | https://hdl.handle.net/1721.1/131775 |
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