Yazar "Marey, Ahmed" seçeneğine göre listele

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  • Küçük Resim
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    PL-GAN: Path loss prediction using generative adversarial networks
    (IEEE-Institute of Electrical and Electronics Engineers Inc., 2022) Marey, Ahmed; Bal, Mustafa; Ateş, Hasan Fehmi; Güntürk, Bahadır Kürşat
    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.
  • Küçük Resim
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    Regression of large-scale path loss parameters using deep neural networks
    (IEEE-Institute of Electrical and Electronics Engineers Inc., 2022) Bal, Mustafa; Marey, Ahmed; Ateş, Hasan Fehmi; Baykaş, Tunçer; Güntürk, Bahadır Kürşat
    Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.