Base Station Location Optimization Based on Genetic Algorithm in CAD System

  • Yanhua WangEmail author
  • Laisheng Xiang
  • Xiyu Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10745)


A good base station deployment plan can help network operators save cost and increase total revenue significantly under the premise of ensuring network quality. But in the past, base station location planning is often manually based on the engineer’s experience. It has a lower efficiency and very high error rate. In this paper, a new method based on genetic algorithm is proposed to optimize base station location. In our work, a CAD system based Google Earth and ACIS is designed to provide data for Genetic algorithm and display the location of base station in the reconstructed terrain. This system which takes three-dimensional geographic coordinates as the input of the algorithm is advanced and different from the traditional method which only uses two-dimensional coordinates, that is, this three-dimensional system can better display the base station location and take the height into consideration. The proposed method is based on a mathematical model of base station location. Genetic Algorithm is used to find the solution of this model so that it can effectively reduce the error rate of base station location.


Genetic Algorithm Base station planning Coverage ACIS Google earth CAD system 



Projected supported by National Natural Science Foundation of China (61472231, 61170038, 61502283, 61640201), Jinan City independent innovation plan project in College and Universities, China (201401202), Ministry of education of Humanities and social science research project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (11CGLJ22, 16BGLJ06).


  1. 1.
    Xie, Q., Liu, X., Yan, X.: Research on station location optimization CAD system based on the cooperative mode. In: Zu, Q., Hu, B. (eds.) HCC 2016. LNCS, vol. 9567, pp. 930–935. Springer, Cham (2016). CrossRefGoogle Scholar
  2. 2.
    Xie, Q., Liu, X., Yan, X.: Base station location optimization based on the Google Earth and ACIS. In: Zu, Q., Hu, B. (eds.) HCC 2016. LNCS, vol. 9567, pp. 487–496. Springer, Cham (2016). CrossRefGoogle Scholar
  3. 3.
    Ren, S., Li, X., Liu, X.: The 3D visual research of improved DEM data based on Google Earth and ACIS. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds.) ICPCA/SWS 2013. LNCS, vol. 8351, pp. 497–507. Springer, Cham (2014). Google Scholar
  4. 4.
    Tao, M., et al.: SA-PSO based optimizing reader deployment in large-scale RFID Systems. J. Netw. Comput. Appl. 52, 90–100 (2015)CrossRefGoogle Scholar
  5. 5.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. John Wiley & Sons, New York (1966)zbMATHGoogle Scholar
  6. 6.
    Schwefel, H.-P.: Numerical Optimization of Computer Models. John Wiley & Sons Inc., New York (1981)zbMATHGoogle Scholar
  7. 7.
    Golberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Boston (1989)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.College of Management Science and EngineeringShandong Normal UniversityJinanChina

Personalised recommendations