Skip to main content
SpringerLink
Log in

An Improved Propagation Prediction Model Based on Okumura-Hata Model

  • Conference paper
  • First Online:
The 8th International Conference on Computer Engineering and Networks (CENet2018) (CENet2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 905))

Included in the following conference series:

  • 795 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dao, N.N., Park, M., Kim, J., Paek, J., Cho, S.: Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for Internet of Things systems. Futur. Gener. Comput. Syst. 93, 877–887 (2018)

    Article  Google Scholar 

  2. Gavrilovska, L., Latkoski, P., Atanasovski, V., Prasad, R., Mihovska, A., Fratu, O., Lazaridis, P.: Radio spectrum: evaluation approaches, coexistence issues and monitoring. Comput. Netw. 121, 1–12 (2017)

    Article  Google Scholar 

  3. Garg, V.: Wireless Communications & Networking. Elsevier, Amsterdam (2010)

    Google Scholar 

  4. Hata, M.: Empirical formula for propagation loss in land mobile radio services. IEEE Trans. Veh. Technol. 29(3), 317–325 (1980)

    Article  Google Scholar 

  5. Nadir, Z., Ahmad, M.I.: Pathloss determination using Okumura-Hata model and cubic regression for missing data for Oman. In: Proceedings of the International Multi-Conference of Engineering and Computer scientist, p. 2 (2010)

    Google Scholar 

  6. Lee, W.C.Y.: Mobile Communications Design Fundamentals. Wiley, Hoboken (2010)

    Google Scholar 

  7. Anderson, L., Trolese, L.: Simplified method for computing knife edge diffraction in the shadow region. IEEE Trans. Antennas Propag. 6(3), 281–286 (1958)

    Article  Google Scholar 

  8. Lee, W.C.Y.: Integrated Wireless Propagation Models. McGraw-Hill Education, New York (2015)

    Google Scholar 

  9. Epstein, J., Peterson, D.W.: An experimental study of wave propagation at 850 MC. Proc. IRE 41(5), 595–611 (1953)

    Article  Google Scholar 

  10. Giovaneli, C.L.: An analysis of simplified solutions for multiple knife-edge diffraction. IEEE Trans. Antennas Propag. 32(3), 297–301 (1984)

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Rui Gao, Yanan Zhao, Yixuan Wang & Tianfeng Yan

  2. Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Rui Gao

Authors
  1. Rui Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianfeng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yixuan Wang .

Editor information

Editors and Affiliations

  1. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Qi Liu

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Mustafa Mısır

  3. School of Computer, Fudan University, Shanghai, China

    Xin Wang

  4. College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, China

    Weiping Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, R., Zhao, Y., Wang, Y., Yan, T. (2020). An Improved Propagation Prediction Model Based on Okumura-Hata Model. In: Liu, Q., Mısır, M., Wang, X., Liu, W. (eds) The 8th International Conference on Computer Engineering and Networks (CENet2018). CENet2018 2018. Advances in Intelligent Systems and Computing, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-030-14680-1_87

Download citation

Publish with us

Policies and ethics

Search

Navigation