PL-GAN: Path loss prediction using generative adversarial networks

Marey, AhmedBal, MustafaAteş, Hasan FehmiGüntürk, Bahadır Kürşat2022-09-162022-09-162022Marey, A., Bal, M., Ateş, H. F. ve Güntürk, B. K. (2022). PL-GAN: Path loss prediction using generative adversarial networks. IEEE Access, 10, 90474-90480. https://doi.org/10.1109/ACCESS.2022.32016432169-3536https://doi.org/10.1109/ACCESS.2022.3201643https://hdl.handle.net/20.500.12511/9716Accurate 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.enAttribution 4.0 Internationalinfo:eu-repo/semantics/openAccessDeep LearningHeight MapsSatellite ImagesGANSChannel Parameter EstimationWireless NetworkRegressionExcess Path LossPL-GAN: Path loss prediction using generative adversarial networksArticle10904749048010.1109/ACCESS.2022.3201643Q20008508446000012-s2.0-85137576600Q1