An Improved Propagation Prediction Model Based on Okumura-Hata Model

  • Rui Gao
  • Yanan Zhao
  • Yixuan WangEmail author
  • Tianfeng Yan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


The explosive development of wireless communications has brought severe challenges to radio spectrum strategy and planning. In the complex electromagnetic environment, accurate spectrum availability estimation and simulation are playing an increasingly important role in economic development. In recent decades, many researchers and engineers presented a large number of mathematical models to attempt to solve this problem. Okumura-Hata model is one of the most popular models for predicting macrocell path loss in a general flat terrain, but is poor in the mountainous terrain. This paper proposes an improved propagation prediction model based on the Okumura-Hata model, considering the effect of ductile diffraction in the hilly terrain environment. Furthermore, we designed a comparative experiment to verify the effectiveness of improved propagation prediction model. Finally, the results of simulation analysis show that this improved propagation prediction model has high accuracy result in mountains terrain environment.


Wireless communications Propagation prediction model Okumura-Hata 



Thanks to the experimental data provided by the Institute of Digital Signal Processing and Software-Defined Radio, Lanzhou Jiaotong University. In addition, the authors gratefully acknowledge the financial support provided by Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (KFKT2018-16), Youth Science Foundation of Lanzhou Jiaotong University under Grant No. 2018003, Innovation Fund Project of Lanzhou Jiaotong University and Tianjin University under Grant No. 2018062, Scientific Research plan projects of Gansu Education Department under Grant No. 2017C-09, Lanzhou Science and Technology Bureau under Grant No. 2018-1-51.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rui Gao
    • 1
    • 2
  • Yanan Zhao
    • 1
  • Yixuan Wang
    • 1
    Email author
  • Tianfeng Yan
    • 1
  1. 1.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouChina
  2. 2.Key Laboratory of Opto-Technology and Intelligent ControlMinistry of Education, Lanzhou Jiaotong UniversityLanzhouChina

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