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RPMA Low-Power Wide-Area Network Planning Method Basing on Data Mining

  • Yao Shen
  • Xiaorong ZhuEmail author
  • Yue Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 258)

Abstract

A network planning method based on data mining was proposed for Random Phase Multiple Access (RPMA) low-power wide-area network (LPWAN) with large density of base stations and uneven traffic distribution. First, a signal quality prediction model was established by using the boosting regression trees algorithm, which was used to extract the coverage distribution spacial pattern of the network. Then, the weighted K-centroids clustering algorithm was utilized to obtain the optimal base station deployment for the current spacial pattern. Finally, according to the total objective function, the best base station topology was determined. Experimental results with the real data sets show that compared with the traditional network planning method, the proposed method can improve the coverage of low-power wide-area networks.

Keywords

Low power wide area network Boosting regression trees Weighted K-centroids Base station deployment 

Notes

Acknowledgements

This work was supported by National Science & Technology Key Project of China (2017ZX03001008), Natural Science Foundation of China (61871237), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0766) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA510005).

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Copyright information

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

Authors and Affiliations

  1. 1.College of Telecommunication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina

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