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)


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.


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



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).


  1. 1.
    Patel, D., Won, M. Experimental study on low power wide area networks (LPWAN) for mobile internet of things. In: IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, pp. 1–5 (2017)Google Scholar
  2. 2.
    Hernandez, D.M, Peralta, G., Manero, L., et al.: Energy and coverage study of LPWAN schemes for industry 4.0. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), Donostia-San Sebastian, pp. 1–6 (2017)Google Scholar
  3. 3.
    Xiong, X., Zheng, K., Xu, R., Xiang, W., Chatzimisios, P.: Low power wide area machine-to-machine networks: key techniques and prototype. Commun. Mag. IEEE 53(9), 64–71 (2015)CrossRefGoogle Scholar
  4. 4.
    Krupka, L., Vojtech, L., Neruda, M.: The issue of LPWAN technology coexistence in IoT environment. In: 2016 17th International Conference on Mechatronics - Mechatronika (ME), Prague, pp. 1–8 (2016)Google Scholar
  5. 5.
    Wang, S., Zhao, W., Wang, C.: Budgeted cell planning for cellular networks with small cells. IEEE Trans. Veh. Technol. 64(10), 4797–4806 (2015)CrossRefGoogle Scholar
  6. 6.
    Ghazzai, H., Yaacoub, E., Alouini, M.S., et al.: Optimized LTE cell planning with varying spatial and temporal user densities. IEEE Trans. Veh. Technol. 65(3), 1575–1589 (2016)CrossRefGoogle Scholar
  7. 7.
    Yang, Z.H., Chen, M., Wen, Y.P., et al.: Cell Planning based on minimized power consumption for lte networks. In: IEEE Wireless Communications and NETWORKING Conference. IEEE (2016)Google Scholar
  8. 8.
    Wang, S., Ran, C.: Rethinking cellular network planning and optimization. IEEE Wirel. Commun. 23(2), 118–125 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wen, R., Yan, W., Zhang, A.N.: Weighted clustering of spatial pattern for optimal logistics hub deployment. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, pp. 3792–3797 (2016)Google Scholar
  11. 11.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRefGoogle Scholar

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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