Applying Genetic Algorithms for Multiradio Wireless Mesh Network Planning

Two main issues that are involved in the performance of multichannel multiradio wireless mesh networks (WMNs) are channel assignment (CA) and multichannel routing (MCR). The joint CA and MCR problem has been proven to be NP-hard. In this paper, we propose to apply genetic algorithms for the CA probl...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2012-06, Vol.61 (5), p.2256-2270
Main Authors: LIN, Ting-Yu, HSIEH, Kai-Chiuan, HUANG, Hsin-Chun
Format: Article
Language:English
Subjects:
Applied sciences
Biological cells
Business and industry local networks
Channels
Computer simulation
Downlink
Encoding
Evolution & development
Exact sciences and technology
Finite element method
Genetic algorithm (GA)
Genetic algorithms
Guidelines
IEEE 802.11
Internet
linear programming (LP)
Logic gates
Multichannel
multiradio wireless mesh networks (WMNs)
network planning
Networks
Networks and services in france and abroad
Organization and planning of networks (techniques and equipments)
Studies
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Transmission and modulation (techniques and equipments)
Wireless communication
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Summary:Two main issues that are involved in the performance of multichannel multiradio wireless mesh networks (WMNs) are channel assignment (CA) and multichannel routing (MCR). The joint CA and MCR problem has been proven to be NP-hard. In this paper, we propose to apply genetic algorithms for the CA problem and tackle MCR using linear-programming techniques. Because CA and MCR tend to interact with each other, to reflect such an interplay property, we evaluate the fitness value of a chromosome (certain CA configuration) in our genetic algorithms (GAs) by computing the linear objective function. Therefore, we successfully decouple the two problems and obtain an optimized CA configuration with a corresponding MCR schedule in polynomial time at the WMN planning stage. We demonstrate the detailed genetic evolution processes for three WMNs with different deployment constraints, which serves as a useful guideline on customizing WMNs under various requirements. Simulation results show that the proposed approach effectively increases the network capacity.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2012.2191166