@inproceedings{SisLab4282,
       booktitle = {2020 14th European Conference on Antennas and Propagation (EuCAP)},
           month = {July},
           title = {VoglerNet: multiple knife-edge diffraction using deep neural network},
          author = {Viet Dung Nguyen and Huy Phan and Ali Mansour and Arnaud Coatanhay},
            year = {2020},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4282/},
        abstract = {Multiple knife-edge diffraction estimation is a fundamental problem in wireless communication. One of the most well-known algorithm for predicting diffraction is Vogler algorithm which has been shown to reach the state-of-the-art results in both simulation and measurement experiments. However, it can not be easily used in practice due to its high computational complexity. In this paper, we propose VoglerNet, a data-driven diffraction estimator, by converting the Vogler algorithm into a deep neural network based system. To train VoglerNet, we propose to minimize a regularized loss function using Levenberg-Marquardt backpropagation in conjunction with a Bayesian regularization. Our numerical experiments show that VoglerNet provides fast solution in order of milliseconds while its performance is very close to that of the classical Vogler algorithm.}
}