5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Decentralized "good neighbor" DSA based on adaptive antenna array interference mitigation diversity: Finite amount of data effects

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  • @INPROCEEDINGS{10.4108/ICST.CROWNCOM2010.9241,
        author={Alexandr M. Kuzminskiy and Yuri I. Abramovich},
        title={Decentralized "good neighbor" DSA based on adaptive antenna array interference mitigation diversity: Finite amount of data effects},
        proceedings={5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2010},
        month={9},
        keywords={Decentralized dynamic spectrum allocation interference mitigation "good neighbor" strategy Markov chain},
        doi={10.4108/ICST.CROWNCOM2010.9241}
    }
    
  • Alexandr M. Kuzminskiy
    Yuri I. Abramovich
    Year: 2010
    Decentralized "good neighbor" DSA based on adaptive antenna array interference mitigation diversity: Finite amount of data effects
    CROWNCOM
    IEEE
    DOI: 10.4108/ICST.CROWNCOM2010.9241
Alexandr M. Kuzminskiy1,*, Yuri I. Abramovich2,*
  • 1: Alcatel-Lucent, The Quadrant, Swindon SN5 7DJ, UK
  • 2: Defence Science and Technology Organization, PO Box 1500, Edinburgh SA 5111, Australia
*Contact email: Alexandr.Kuzminskiy@alcatel-lucent.com, Yuri.Abramovich@dsto.defence.gov.au

Abstract

Finite amount of data spectrum sensing effects are addressed in decentralized dynamic spectrum allocation (DSA) that exploits adaptive antenna array interference mitigation (IM) diversity at the receiver. The system is based on spectrally efficient filter bank multi-carrier (FBMC) PHY and consists of base stations (BSs) that use a "good neighbor" rule-regulated strategy to optimize uplink frequency allocation to their subscriber stations (SSs) to achieve the least impact of IM on the useful signal. A spectrum sensing protocol is proposed and DSA efficiency is studied by means of semi-analytical Markov chain approach for low-dimension networks and statistical simulations for higher-dimension spectrum sharing systems.