Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Rapid Self-Configuration and Optimization of Mobile Communication Network Base Station using Artificial Intelligent and SON Technology

인공지능과 자율운용 기술을 이용한 긴급형 이동통신 기지국 자율설정 및 최적화

  • Received : 2022.07.27
  • Accepted : 2022.08.14
  • Published : 2022.09.30
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Abstract

It is important to quickly and accurately build a disaster network or tactical mobile communication network adapting to the field. In configuring the traditional wireless communication systems, the parameters of the base station are set through cell planning. However, for cell planning, information on the environment must be established in advance. If parameters which are not appropriate for the field are used, because they are not reflected in cell planning, additional optimization must be carried out to solve problems and improve performance after network construction. In this paper, we present a rapid mobile communication network construction and optimization method using artificial intelligence and SON technologies in mobile communication base stations. After automatically setting the base station parameters using the CNN model that classifies the terrain with path loss prediction through the DNN model from the location of the base station and the measurement information, the path loss model enables continuous overage/capacity optimization.

긴급 상황에 대비하는 재난망이나 전술 이동통신 네트워크는 현장에 적응하여 신속하고 정확하게 구축하는 것이 중요하다. 전통적인 무선통신 시스템을 구성하기 위해서는 셀 플래닝 장비를 통해 기지국의 파라미터를 설정한다. 하지만 셀 플래닝을 위해서는 환경에 대한 정보나 데이터가 사전에 구축되어 있어야 하며, 셀 플래닝에 반영되지 않아 현장에 맞지 않는 파라미터가 사용되면 네트워크 구축 후 문제의 해결 및 성능 향상을 위해서 별도의 최적화가 진행되어야 한다. 이 논문에서는 이동통신 기지국에서의 인공지능(AI)과 자율운용(SON) 기술을 사용한 신속한 이동통신망 구축 및 최적화 방법을 제시한다. 기지국의 위치와 단말의 측정 정보를 이용한 DNN 모델을 통해 경로 손실 예측을 수행하여 지형을 구분하는 CNN 모델을 기지국 파라미터를 자동으로 설정한 후, 운용 중에 수집되는 데이터로 경로 손실 모델을 학습시키며 이를 이용해 Coverage/Capacity 최적화를 지속적으로 수행할 수 있도록 한다.

Keywords

Acknowledgement

This work was supported by grant No. UC190001D (AI based mobile communication network autonomous operation technology considering tactical situations) from DAPA (Defense Acquisition Program Administration) and DITC (Defense Industry Technology Center)

References

  1. ETSI, "Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN)," 3GPP, Sophia Antipolis Valbonne, France, Technical Report 3GPP TS 36.300, 2020.
  2. ETSI, "Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Self-configuration and self-optimizing network (SON) user cases and solutions," 3GPP, Sophia Antipolis Valbonne, France, Technical Report 3GPP TR 36.902, 2011.
  3. J. J. Kim, H. J. Lee, and S. H. Ji, "Method of Predicting Path Loss and Base Station Topography Classification using Artificial Intelligent in Mobile Communication Systems," Journal of the Korea Institute of Information and Communication Engineering, vol. 26, no. 5, pp. 702-712, May. 2022.
  4. R. Levie, C. Yapar, G. Kutyniok, and G. Caire, "Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Scheduling," in Proceedings of ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, pp. 8678-8682, 2020.
  5. C. Zhang, P. Patras, and H. Haddadi, "Deep Learning in Mobile and Wireless Networking: A Survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, Mar. 2019. https://doi.org/10.1109/COMST.2019.2904897
  6. M. M. Butt, A. Rao, and D. Yoon, "RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G," in Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, pp. 1-7. 2020.
  7. G. K. Venkatesh and P. V. Rao, "Optimizing handover in LTE using SON system by handling mobility robustness," in Proceedings of 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, pp. 2231-2235, 2017.
  8. ETSI, "Evolved Universal Terrestrial Radio Access (E-UTRA) Radio Resource Control (RRC) Protocol specification," 3GPP, Sophia Antipolis Valbonne, France, Techinical Report 3GPP 36.331, 2020.
  9. Scipy.optimize.fmin_slsqp [Internet]. Available: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_slsqp.html.