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A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks

Author

Listed:
  • Djorwé Témoa

    (Department of Computer Science and Telecommunications, National Advanced Engineering School, University of Maroua, 46 Maroua, Cameroon)

  • Anna Förster

    (Communication Networks, University of Bremen, 28359 Bremen, Germany)

  • Kolyang

    (Department of Computer Science, Higher Teachers’ Training College, University of Maroua, 46 Maroua, Cameroon)

  • Serge Doka Yamigno

    (Faculty of Science, University of Ngaoundere, 454 Ngaoundere, Cameroon)

Abstract

Long Term Evolution networks, which are cellular networks, are subject to many impairments due to the nature of the transmission channel used, i.e. the air. Intercell interference is the main impairment faced by Long Term Evolution networks as it uses frequency reuse one scheme, where the whole bandwidth is used in each cell. In this paper, we propose a full dynamic intercell interference coordination scheme with no bandwidth partitioning for downlink Long Term Evolution networks. We use a reinforcement learning approach. The proposed scheme is a joint resource allocation and power allocation scheme and its purpose is to minimize intercell interference in Long Term Evolution networks. Performances of proposed scheme shows quality of service improvement in terms of SINR, packet loss and delay compared to other algorithms.

Suggested Citation

  • Djorwé Témoa & Anna Förster & Kolyang & Serge Doka Yamigno, 2019. "A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks," Future Internet, MDPI, vol. 11(1), pages 1-23, January.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:1:p:19-:d:198469
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    References listed on IDEAS

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    1. Georgios Katsinis & Eirini Eleni Tsiropoulou & Symeon Papavassiliou, 2017. "Multicell Interference Management in Device to Device Underlay Cellular Networks," Future Internet, MDPI, vol. 9(3), pages 1-20, August.
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