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A Machine Learning Based Temporary Base Station (BS) Placement Scheme in Booming Customers Circumstance

  • Qinglong DaiEmail author
  • Li Zhu
  • Peng Wang
  • Guodong Li
  • Jianjun Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 262)

Abstract

Explosive increase of terminal users and the amount of data traffic give a great challenge for Internet service providers (ISPs). At the same time, this big data also brings an opportunity for ISPs. How to solve network planning problem in emergency or clogging situation, based on big data? In this paper, we try to realize effective and flexible temporary base station (BS) placement through machine learning in a booming customers situation, with ISPs’ massive data. A machine learning based temporary BS placement scheme is presented. A K-means based model training algorithm is put forward, as a vital part of machine learning based temporary BS placement scheme. K-means algorithm is selected as a representative example of machine learning algorithm. The performances of BS position with random starting point, BS position iteration, average path length with different parameters, are conducted to prove the availability of our work.

Keywords

Network planning Machine learning Big data K-means algorithm 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Qinglong Dai
    • 1
    Email author
  • Li Zhu
    • 2
    • 3
  • Peng Wang
    • 1
  • Guodong Li
    • 1
  • Jianjun Chen
    • 1
  1. 1.China Academy of Electronics and Information TechnologyBeijingChina
  2. 2.China Transport Telecommunications and Information CenterBeijingChina
  3. 3.People’s Public Security University of ChinaBeijingChina

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