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Communication dans un congrès

VoglerNet: multiple knife-edge diffraction using deep neural network

Viet-Dung Nguyen 1 Huy Phan 2 Ali Mansour 3 Arnaud Coatanhay 1
1 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENSTAB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-02924177
Contributeur : Marie Briec <>
Soumis le : jeudi 27 août 2020 - 17:27:44
Dernière modification le : samedi 5 septembre 2020 - 03:24:33

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Viet-Dung Nguyen, Huy Phan, Ali Mansour, Arnaud Coatanhay. VoglerNet: multiple knife-edge diffraction using deep neural network. 2020 14th European Conference on Antennas and Propagation (EuCAP), Mar 2020, Copenhagen, France. pp.1-5, ⟨10.23919/EuCAP48036.2020.9135548⟩. ⟨hal-02924177⟩

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