8th International Conference on Communications and Networking in China

Research Article

Game-theoretical Frequency Reuse Method for Complex Cognitive Femto-cell Network

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1109/ChinaCom.2013.6694613,
    author={Xiong Zhou and Suili Feng and Yuehua Ding and Yun Liu and Yide Wang and YongZhong Zhang},
    title={Game-theoretical Frequency Reuse Method for Complex Cognitive Femto-cell Network},
    proceedings={8th International Conference on Communications and Networking in China},
    publisher={IEEE},
    proceedings_a={CHINACOM},
    year={2013},
    month={11},
    keywords={game theory femto-cell network frequency collision self-organizing clustering},
    doi={10.1109/ChinaCom.2013.6694613}
}
  • Xiong Zhou
    Suili Feng
    Yuehua Ding
    Yun Liu
    Yide Wang
    YongZhong Zhang
    Year: 2013
    Game-theoretical Frequency Reuse Method for Complex Cognitive Femto-cell Network
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2013.6694613
Xiong Zhou1, Suili Feng1,*, Yuehua Ding1, Yun Liu1, Yide Wang2, YongZhong Zhang3
  • 1: South China University of Technology
  • 2: Université de Nantes
  • 3: China Electronics Technology Group Corporation No.7 Research Institute
*Contact email: fengsl@scut.edu.cn

Abstract

Generally, femto-cell base stations (FBS) are installed and maintained by users. Hence, the structure and scale of a femto-cell network are extremely variable. Frequency collision usually occurs between neighboring FBSs in such a complex femto-cell network, where fraction frequency reuse (FFR) method is used to eliminate the co-channel interference. In this paper, a novel frequency reuse method is proposed for femto-cell network by using game theory. The proposed method consists of self-organizing clustering and optimal frequency resources allocation inside a cluster. Simulation results show that the average capacity of femto-cell network can be effectively increased by implementing the proposed method.

Keywords
game theory femto-cell network frequency collision self-organizing clustering
Published
2013-11-14
Publisher
IEEE
http://dx.doi.org/10.1109/ChinaCom.2013.6694613
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