TY  - CONF
ID  - SisLab4282
UR  - https://ieeexplore.ieee.org/document/9135548/
A1  - Nguyen, Viet Dung
A1  - Phan, Huy
A1  - Mansour, Ali
A1  - Coatanhay, Arnaud
Y1  - 2020/07/08/
N2  - 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.
TI  - VoglerNet: multiple knife-edge diffraction using deep neural network
M2  - Copenhagen, Denmark,
AV  - public
T2  - 2020 14th European Conference on Antennas and Propagation (EuCAP)
ER  -