eprintid: 4282
rev_number: 16
eprint_status: archive
userid: 381
dir: disk0/00/00/42/82
datestamp: 2020-12-17 05:22:34
lastmod: 2020-12-17 05:22:34
status_changed: 2020-12-17 05:22:34
type: conference_item
metadata_visibility: show
creators_name: Nguyen, Viet Dung
creators_name: Phan, Huy
creators_name: Mansour, Ali
creators_name: Coatanhay, Arnaud
creators_id: nvdung@vnu.edu.vn
title: VoglerNet: multiple knife-edge diffraction using deep neural network
ispublished: pub
subjects: Communications
divisions: avitech
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.
date: 2020-07-08
date_type: published
official_url: https://ieeexplore.ieee.org/document/9135548/
full_text_status: public
pres_type: paper
event_title: 2020 14th European Conference on Antennas and Propagation (EuCAP)
event_location: Copenhagen, Denmark,
event_dates: 15-20 March 2020
event_type: conference
refereed: TRUE
citation:   Nguyen, Viet Dung and Phan, Huy and Mansour, Ali and Coatanhay, Arnaud  (2020) VoglerNet: multiple knife-edge diffraction using deep neural network.  In: 2020 14th European Conference on Antennas and Propagation (EuCAP), 15-20 March 2020, Copenhagen, Denmark,.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4282/1/Dung2020.pdf