%0 Conference Paper
%A Nguyen, Viet Dung
%A Phan, Huy
%A Mansour, Ali
%A Coatanhay, Arnaud
%B 2020 14th European Conference on Antennas and Propagation (EuCAP)
%C Copenhagen, Denmark,
%D 2020
%F SisLab:4282
%T VoglerNet: multiple knife-edge diffraction using deep neural network
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4282/
%X 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.