전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Self-Organizing Network에서 기계학습 연구동향-I

Research Status of Machine Learning for Self-Organizing Network - I

  • 권동승 (지능형고밀집스몰셀연구실) ;
  • 나지현 (지능형고밀집스몰셀연구실)
  • 발행 : 2020.08.01
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초록

In this study, a machine learning (ML) algorithm is analyzed and summarized as a self-organizing network (SON) realization technology that can minimize expert intervention in the planning, configuration, and optimization of mobile communication networks. First, the basic concept of the ML algorithm in which areas of the SON of this algorithm are applied, is briefly summarized. In addition, the requirements and performance metrics for ML are summarized from the SON perspective, and the ML algorithm that has hitherto been applied to an SON achieves a performance in terms of the SON performance metrics.

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과제정보

이 논문은 2020년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2020-0-009454, 5G 스몰셀을 위한 인공지능 기반 자율구성 네트워크(SON 기술 개발)].

참고문헌

  1. R. Barco, V. Wilee, and L. Diez, "System for Automated Diagnosis in Cellular Networks based on Performance Indicators," Eur. Trans. Telecommun., vol. 16, no. 5, 2005, pp. 399-409. https://doi.org/10.1002/ett.1060
  2. J. Laiho et al., "Advanced Analysis Methods for 3G Cellular Networks," IEEE Trans. Wireless Commun., vol. 4, no. 3, 2005, pp. 930-942. https://doi.org/10.1109/TWC.2005.847088
  3. O. G. Aliu et al., "A Survey of Self-Organisation in Future Cellular Networks," IEEE Commun. Surveys Tuts., vol. 15, no. 1, 2013, pp. 336-361. https://doi.org/10.1109/SURV.2012.021312.00116
  4. S. Bi et al., "Wireless Communications in the Era of Big Sata," IEEE Commun. Mag., vol. 53, no. 10, Oct. 2015, pp. 190-199. https://doi.org/10.1109/MCOM.2015.7295483
  5. M. Peng et al., "Self-Configuration and Self-Optimization in LTE-Advanced Heterogeneous Networks," IEEE Commun. Mag., vol. 51, no. 5, May. 2013, pp. 36-45. https://doi.org/10.1109/MCOM.2013.6515045
  6. R. Barco et al., "A Unified Framework for Self-Healing in Wireless Networks," IEEE Commun. Maga., vol. 50, no. 12, Dec. 2012, pp. 134-142. https://doi.org/10.1109/MCOM.2012.6384463
  7. A. Tall et al., "Distributed Coordination of Self-Organizing Mechanisms in Communication Networks," IEEE Trans. Contr. Netw. Syst., vol. 1, no. 4, Dec. 2014, pp. 328-337. https://doi.org/10.1109/TCNS.2014.2357511
  8. I. Karla, "Resolving SON Interactions Via Self-Learning Prediction in Cellular Wireless Networks," in Proc. Int. Conf. Wireless Commun. Netw. Mobile Comput., Shanghai, China, Sept. 2012, pp. 1-6.
  9. H. Y. Lateef, A. Lmran, and A. Abu-dayya, "A Framework for Classification of Self-Organising Network Conflicts and Coordination Algorithms," in Proc. IEEE Annu. Int. Symp. PIMRC, London, UK, Sept. 2013, pp. 2898-2903.
  10. P. Wainio and K. Seppanen, "Self-Optimizing Last-Mile Backhaul Network for 5G Small Cells," in Proc. IEEE Int. Conf. Commun. Workshops, Kuala Lumpur, Malaysia, May. 2016.
  11. P. V. Klaine et al., "A survey of machine learning techniques applied to self-organizing cellular networks," IEEE Commun. Surveys Tutorial, vol. 19, no. 4, July. 2017, pp. 2392-2431. https://doi.org/10.1109/COMST.2017.2727878