A semi-dynamic, game based and interference aware channel assignment for multi-radio multi-channel wireless mesh networks

Channel assignment (CA) has been extensively researched for multi-radio wireless mesh networks (WMNs), but it is still very challenging when it comes to its implementation. In this paper we propose a semi-dynamic and distributed CA mechanism called semi-dynamic interference aware channel assignment...

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Detalles Bibliográficos
Autores: Amiri Nezhad, Maryam, Cerdà Alabern, Llorenç, Bellalta, Boris, Guerrero Zapata, Manel
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2013
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/45201
Acceso en línea:http://hdl.handle.net/10230/45201
http://dx.doi.org/10.1504/IJAHUC.2013.058237
Access Level:acceso abierto
Palabra clave:Channel assignment
Multi-radio
Multi-channel
Wireless mesh network
Game theory
Real-time learning
Descripción
Sumario:Channel assignment (CA) has been extensively researched for multi-radio wireless mesh networks (WMNs), but it is still very challenging when it comes to its implementation. In this paper we propose a semi-dynamic and distributed CA mechanism called semi-dynamic interference aware channel assignment (SICA) based on game theory formulation. SICA is an interference aware, distributed CA which preserves the network connectivity without relying on a common channel nor central node for coordination between mesh routers. SICA applies a real time learner algorithm which assumes that nodes do not have perfect information about the network topology. To the best of our knowledge this is the first game formulation of CA which takes the co-channel interference into account. We have simulated SICA and compared against other channel assignment mechanisms proposed in the literature. Simulation results show that SICA outperforms other mechanisms.