6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Distributed Channel Assignment Algorithm based on Simulated Annealing for Uncoordinated OSA-Enabled WLANs

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2011.245906,
        author={Francisco Novillo and Ramon Ferrus},
        title={Distributed Channel Assignment Algorithm based on Simulated Annealing for Uncoordinated OSA-Enabled WLANs},
        proceedings={6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2012},
        month={5},
        keywords={channel assignment; distributed algorithm; osa; simulated annealing; spectrum heterogeneity; wlan},
        doi={10.4108/icst.crowncom.2011.245906}
    }
    
  • Francisco Novillo
    Ramon Ferrus
    Year: 2012
    Distributed Channel Assignment Algorithm based on Simulated Annealing for Uncoordinated OSA-Enabled WLANs
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2011.245906
Francisco Novillo1,*, Ramon Ferrus1
  • 1: Technical University of Catalonia (UPC)
*Contact email: fnovillo@tsc.upc.edu

Abstract

A promising approach to alleviate ISM band congestion problems in highly dense WLAN scenarios consists of exploiting opportunistic spectrum access (OSA) to underutilized bands under a primary-secondary model. This paper develops a distributed channel assignment algorithm valid for uncoordinated WLAN deployments where access points do not follow any specific planning and they could belong to different administrative domains. Unlike existing channel assignment schemes proposed for legacy WLANs, channel assignment mechanisms for OSA-enabled WLAN should address two distinguishing issues: channel prioritization and spectrum heterogeneity. Over such a basis, this paper develops and assesses the performance of a distributed channel assignment algorithm that is able to exploit both channel prioritization and spectrum heterogeneity concepts. In particular, the algorithm is based on a distributed adaptation of the simulated annealing metaheuristic algorithm commonly used in global optimization problems.