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VoglerNet: multiple knife-edge diffraction using deep neural network

Viet Dung Nguyen
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Ali Mansour
Arnaud Coatanhay


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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Dates et versions

hal-02924177 , version 1 (27-08-2020)



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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