Skip to main content

ML-based delay–angle-joint path loss prediction for UAV mmWave channels


Path loss is important for the unmanned aerial vehicle (UAV) placement, trajectory optimization, and power allocation in UAV-aided communications. By considering both the factors of path delay and reflection angle (RA) in the non-line-of-sight (NLoS) paths, a new machine learning-based delay–angle-joint path loss prediction method for UAV mmWave channels is proposed. The one-input back propagation based neural network (BPNN) and two-input BPNN are built to predict the path power for line-of-sight (LoS) case and NLoS case, respectively. Meanwhile, a data acquisition method is developed to obtain massive data set for training the BPNN. According to the geometric information of digital map, a calculation method for path delay and RA is also proposed to drive the BPNN. The proposed method is simulated and analyzed based on ray-tracing (RT) simulated data under a typical urban scenario at 28 GHz. The 2D relationship of power–delay for the LoS case and 3D relationship of power–delay–RA for the NLoS case are obtained through the trained BPNN, which are well consistent with the validation set of RT data and outperform the traditional methods, i.e., 3GPP model and exponential model. Moreover, it’s found that the path power rapidly decreases when RA is \(65^{\circ }\)\(75^{\circ }\) under the simulation scenario, which could be an important reference for transceiver placement and route planning to reduce the impact of NLoS paths in the UAV channel.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Availability of data and material

Not applicable

Code availibility

Not applicable


  1. 1.

    Zeng, Y., Wu, Q., & Zhang, R. (2019). Accessing from the sky: A tutorial on UAV communications for 5G and beyond. Proceedings of the IEEE, 107(12), 2327–2375.

    Article  Google Scholar 

  2. 2.

    Li, B., Fei, Z., & Zhang, Y. (2018). UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet of Things Journal, 6(2), 2241–2263.

    Article  Google Scholar 

  3. 3.

    Zhang, L., Zhao, H., Hou, S., Zhao, Z., Xu, H., et al. (2019). A survey on 5G millimeter wave communications for UAV-assisted wireless networks. IEEE Access, 7, 117460–117504.

    Article  Google Scholar 

  4. 4.

    You, X., Wang, C., Huang, J., et al. (2021). Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China Information Sciences, 41(1), 1–74.

    Google Scholar 

  5. 5.

    Shakoor, S., Kaleem, Z., Do, D. T., Dobre, O. A., & Jamalipour, A. (2021). Joint optimization of UAV 3-D placement and path-loss factor for energy-efficient maximal coverage. IEEE Internet of Things Journal, 8(12), 9776–9786.

    Article  Google Scholar 

  6. 6.

    Liu, X., Wang, J., Zhao, N., Chen, Y., Zhang, S., et al. (2019). Placement and power allocation for NOMA-UAV networks. IEEE Wireless Communications Letters, 8(3), 965–968.

    Article  Google Scholar 

  7. 7.

    Liu, X., & Zhang, X. (2020). NOMA-based resource allocation for cluster-based cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 16(8), 5379–5388.

    Article  Google Scholar 

  8. 8.

    Liu, X., Zhai, X. B., Lu, W., & Wu, C. (2021). QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA. IEEE Transactions on Industrial Informatics, 17(3), 2052–2061.

    Article  Google Scholar 

  9. 9.

    Liu, X., & Zhang, X. (2019). Rate and energy efficiency improvements for 5G-based IoT with simultaneous transfer. IEEE Internet of Things Journal, 6(4), 5971–5980.

    Article  Google Scholar 

  10. 10.

    Yang, S., Liu, Y., Li, S., & Sun, D. (2018). Simulation and analysis of 60 GHz millimeter wave propagation characteristics in laboratory environment. In Proceedings of the ICMMT’18 (pp. 1–3). Chengdu, China.

  11. 11.

    Hossain, F., Geok, T. K., Rahman, T. A., Hindia, M. N., Dimyati, K., et al. (2019). A smart 3D RT method: Indoor radio wave propagation modelling at 28 GHz. Symmetry, 11(4), 510.

    Article  Google Scholar 

  12. 12.

    He, D., Wang, L., Guan, K., Ai, B., Kim, J., & Zhong, Z. (2019). Channel characterization for mmwave vehicle-to-infrastructure communications in urban street environment. In Proccedings of EuCAP’19 (pp. 1–5). Krakow, Poland.

  13. 13.

    Ullah, U., Kamboh, U. R., Hossain, F., & Danish, M. (2020). Outdoor-to-indoor and indoor-to-indoor propagation path loss modeling using smart 3D ray tracing algorithm at 28 GHz mmWave. Arabian Journal for Science and Engineering, 45(12), 10223–10232.

    Article  Google Scholar 

  14. 14.

    Khawaja, W., Ozdemir, O., & Guvenc, I. (2018). Temporal and spatial characteristics of mm Wave propagation channels for UAVs. In Proceedings of GSMM’18 (pp. 1–6). Boulder, CO, USA.

  15. 15.

    Cheng, L., Zhu, Q., Wang, C.-X., Zhong, W., Hua, B., et al. (2020). Modeling and simulation for UAV air-to-ground mmWave channels. In Proceedings of EuCAP’20 (pp. 1–5). Copenhagen.

  16. 16.

    Zhu, Q., Jiang, S., Wang, C. X., Hua, B., Mao, K.. et al. (2020). Effects of digital map on the RT-based channel model for UAV mmWave communications. In Proceedings of the IWCMC’20 (pp. 1648–1653). Limassol, Cyprus.

  17. 17.

    Zhu, Q., Wang, C., Hua, B., Mao, K., Jiang, S., et al. (2021). 3GPP TR 38.901 channel model. In The Wiley 5G ref: The essential 5G reference online (pp. 1–35). Wiley Press.

  18. 18.

    WINNER+, WINNER+ final channel models D5.3 V1.0. Tech. Rep. (2010). [Online]. Available

  19. 19.

    Cui, Z., Briso-Rodrlguez, C., Guan, K., Zhong, Z., & Quitin, F. (2020). Multifrequency air-to-ground channel measurements and analysis for UAV communication systems. IEEE Access, 8, 110565–110574.

    Article  Google Scholar 

  20. 20.

    Zhu, Q., Li, H., Fu, Y., Wang, C., Tan, Y., et al. (2018). A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator. IEEE Transactions on Communications, 66(9), 3865–3878.

    Article  Google Scholar 

  21. 21.

    Zhu, Q., Wang, Y., Jiang, K., Chen, X., Zhong, W., & Ahmed, N. (2019). 3D non-stationary geometry-based multi-input multi-output channel model for UAV-ground communication systems. IET Microwaves, Antennas & Propagation, 13(8), 1104–1112.

    Article  Google Scholar 

  22. 22.

    Michailidis, E. T., Nomikos, N., Trakadas, P., & Kanatas, A. G. (2020). Three-dimensional modeling of mmWave doubly Massive MIMO aerial fading channels. IEEE Transactions on Vehicular Technology, 69(2), 1190–1202.

    Article  Google Scholar 

  23. 23.

    Chang, H., Wang, C., Liu, Y., Huang, J., Sun, J., et al. (2020). A novel non-stationary 6G UAV-to-ground wireless channel model with 3D arbitrary trajectory changes. IEEE Internet of Things Journal, 4662(4), 1–1.

    Google Scholar 

  24. 24.

    Aldossari, S. M., & Chen, K. C. (2019). Machine learning for wireless communication channel modeling: An overview. Wireless Personal Communications, 106(1), 41–70.

    Article  Google Scholar 

  25. 25.

    Li, W., Zhang, J., Ma, X., Zhang, Y., Huang, H., & Cheng, Y. (2020). The way to apply machine learning to IoT driven wireless network from channel perspective. China Communications, 16(1), 148–164.

    Google Scholar 

  26. 26.

    Navabi, S., Wang, C., Bursalioglu, O. Y., & Papadopoulos, H. (2018). Predicting wireless channel features using neural networks. In Proceedings of the ICC’18 (pp. 1–6). MO, USA.

  27. 27.

    Yang, M., Ai, B., He, R., Huang, C., Ma, Z., et al. (2021). Machine-learning-based fast angle-of-arrival recognition for vehicular communications. IEEE Transactions on Vehicular Technology, 70(2), 1592–1605.

    Article  Google Scholar 

  28. 28.

    Thrane, J., Zibar, D., & Christiansen, H. L. (2020). Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz. IEEE Access, 8, 7925–7936.

    Article  Google Scholar 

  29. 29.

    Sotiroudis, S. P., Sarigiannidis, P., Goudos, S. K., & Siakavara, K. (2021). Fusing diverse input modalities for path loss prediction: A deep learning approach. IEEE Access, 9, 30441–30451.

    Article  Google Scholar 

  30. 30.

    Xia, W., Rangan, S., Mezzavillla, M., Lozano, A., Geraci, G., et al. (2020). Generative neural network channel modeling for millimeter-wave UAV communication. arXiv preprint, pp. 1–10.

  31. 31.

    Zhao, X., Du, F., Geng, S., Fu, Z., Wang, Z., et al. (2020). Playback of 5G and beyond measured MIMO channels by an ANN-based modeling and simulation framework. IEEE Journal on Selected Areas in Communications, 38(9), 1945–1954.

    Article  Google Scholar 

  32. 32.

    Yang, G., Zhang, Y., He, Z., Wen, J., Ji, Z., & Li, Y. (2019). Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels. IET Microwaves, Antennas & Propagation, 13(8), 1113–1121.

    Article  Google Scholar 

  33. 33.

    Zhong, Z., Li, C., Zhao, J., & Zhang, X. (2017). Height-dependent path loss model and large-scale characteristics analysis of 28 GHz and 38.6 GHz in urban micro scenarios. In Proceedings of the EUCAP’17 (pp. 1818–1822).

  34. 34.

    Zhu, Q., Yao, M., Bai, F., Chen, X., Zhong, W., et al. (2021). A general altitude-dependent path loss model for UAV-to-ground millimeter-wave communications. Frontiers of Information Technology & Electronic Engineering, 22(6), 767–776.

    Article  Google Scholar 

  35. 35.

    Cui, Z., Briso-Rodriguez, C., Guan, K., Calvo-Ramirez, C., Ai, B., et al. (2019). Measurement-based modeling and analysis of UAV air-ground channels at 1 and 4 GHz. IEEE Antennas and Wireless Propagation Letters, 18(9), 1804–1808.

    Article  Google Scholar 

  36. 36.

    Mao, K., Zhu, Q., Song, M., Ning, B., Hua, B., et al. (2020) A novel non-stationary channel model for UAV-to-vehicle mmWave beam communications. In Proceedings of the MLICOM’20 (pp. 471–484). Shenzhen, China.

Download references


This work was supported in part by the NSFC Key Scientific Instrument and Equipment Development Project under Grant No. 61827801, in part by Natural Science Foundation of Jiangsu Province, No. BK20211182, and in part by the Fundamental Research Funds for the Central Universities, Nos. NS2020026 and NS2020063.

Author information




Software and Writing-original draft, K. Mao; Methodology, B. Ning; Conceptualization and funding acquisition, Q. Zhu; Investigation, X. Ye; Validation, H. Li; Writing-review and editing, M. Song and B. Hua. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Qiuming Zhu.

Ethics declarations

Conflict of interest

No conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mao, K., Ning, B., Zhu, Q. et al. ML-based delay–angle-joint path loss prediction for UAV mmWave channels. Wireless Netw (2021).

Download citation


  • UAV mmWave channel
  • Path loss
  • Reflection angle (RA)
  • Machine learning
  • Back propagation based neural network (BPNN)