PL-GAN: Path Loss Prediction Using Generative Adversarial Networks

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Marey A., Bal M., Ates H. F., GÜNTÜRK B. K.

IEEE Access, vol.10, pp.90474-90480, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2022
  • Doi Number: 10.1109/access.2022.3201643
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.90474-90480
  • Keywords: Satellites, Receivers, Generative adversarial networks, Deep learning, Generators, Predictive models, Solid modeling, Parameter estimation, Wireless networks, Deep learning, Communication systems, Deep learning, height maps, satellite images, GANS, channel parameter estimation, wireless network, regression, excess path loss, air-to-ground communication system
  • Istanbul Medipol University Affiliated: Yes


Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we present a deep learning based approach with clear advantages over the existing ones. The proposed method is based on the Generative Adversarial Network (GAN) technique to predict path loss map of a target area from the satellite image or the height map of the area. The proposed method produces the path loss map of the entire target area in a single inference, with accuracy close to the one produced by ray tracing simulations. The method is tested at 900MHz transmission frequency; the trained model and source codes are publicly available on a Github page.