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MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how

Author

Listed:
  • Yuri V. L. Melo

    (Federal University of Ceará)

  • Vicente A. Sousa

    (Federal University of Rio Grande do Norte)

  • Tarcisio F. Maciel

    (Federal University of Ceará)

Abstract

Mobile communications are preparing for the incredible changes from $$4\mathrm{th} \,\hbox {Generation (4G)}$$ 4 th Generation (4G) to $$5\mathrm{th}\hbox { Generation (5G)}$$ 5 th Generation (5G) in the coming years. In this new generation, co-channel interference is one of the critical challenges to be tackled due to network densification by providing high data rates through several macro and small cells working together, configuring the so-called $$\hbox {Heterogeneous Network (HetNet)}$$ Heterogeneous Network (HetNet) . The umbrella of 3GPP system (including LTE and its evolution towards 5G) provides the $$\hbox {Almost Blank Subframe (ABS)}$$ Almost Blank Subframe (ABS) as a scheme of the $$\hbox {Enhanced Inter-Cell Interference Coordination (e-ICIC)}$$ Enhanced Inter-Cell Interference Coordination (e-ICIC) framework to mitigate interference among macro and small cells. The ABS mutes some of the macro cell transmissions in selected subframes to decrease interference to small cells, thus orthogonalizing macro and small cell transmissions in time-domain. In this paper, we use a $$\hbox {moving average convergence/divergence}$$ moving average convergence/divergence technique based on trading know-how to propose a $$\hbox {real time ABS e-ICIC}$$ real time ABS e-ICIC algorithm. Our proof-of-concept simulation results show relative capacity gains of 112% compared to the case without $$\hbox {ABS}$$ ABS .

Suggested Citation

  • Yuri V. L. Melo & Vicente A. Sousa & Tarcisio F. Maciel, 2021. "MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(3), pages 391-402, March.
    • Handle: RePEc:spr:telsys:v:76:y:2021:i:3:d:10.1007_s11235-020-00725-2
    DOI: 10.1007/s11235-020-00725-2
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    References listed on IDEAS

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    1. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    2. Paskalis Glabadanidis, 2015. "Market Timing and Moving Averages," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-35983-4.
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    Keywords

    LTE; ABS; e-ICIC; Trend; Trading; MAC; MACD;
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