• DocumentCode
    6843
  • Title

    Dynamic Coalition Formation for Network MIMO in Small Cell Networks

  • Author

    Guruacharya, Sudarshan ; Niyato, Dusit ; Bennis, Mehdi ; Dong In Kim

  • Author_Institution
    Sch. of Comput. Eng., Nanyang Technol. Univ. (NTU), Singapore, Singapore
  • Volume
    12
  • Issue
    10
  • fYear
    2013
  • fDate
    Oct-13
  • Firstpage
    5360
  • Lastpage
    5372
  • Abstract
    In this paper, we apply the concepts of network multiple-input-multiple-output (MIMO) to small cell networks. To do so, the issue of imperfect channel state information (CSI) at the transmitter is considered when frequency-division duplexing is used, for which the feedback channel is limited. We first introduce a regret based learning approach to optimize the transmit beamforming parameters for the cases when the feedback channel is temporarily unavailable during deep fades. We then propose a coalition formation game model to cluster the small cell base stations so that they can perform cluster-wise joint beamforming. We take the tit{recursive core} as the solution concept of the coalition formation game. To obtain the recursive core, we first consider a typical merge-split algorithm. However, we show that this algorithm can be unstable. Alternatively, we adopt the merge-only algorithm which guarantees the formation stability and show that its outcome belongs to the recursive core. Finally, we analyze the average number and the average size of coalitions that can form during such a coalition formation process. Numerical simulations are given to illustrate the behavior of the coalition formation among small cell base stations.
  • Keywords
    MIMO communication; array signal processing; cellular radio; feedback; frequency division multiplexing; game theory; learning (artificial intelligence); numerical analysis; wireless channels; CSI-T; cluster-wise joint beamforming; coalition formation game model; dynamic coalition formation; feedback channel; formation stability; frequency-division duplexing; imperfect channel state information at the transmitter; merge-split algorithm; network MIMO; network multiple-input-multiple-output system; numerical simulations; regret based learning approach; small cell base stations; small cell networks; transmit beamforming parameters; Array signal processing; Base stations; Games; Interference; MIMO; Scattering; Vectors; Small cell network; beamforming; clustering; cooperative game theory; network MIMO; regret based learning;
  • fLanguage
    English
  • Journal_Title
    Wireless Communications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1536-1276
  • Type

    jour

  • DOI
    10.1109/TWC.2013.090513.130516
  • Filename
    6596078