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Daniel Fernandes
Researcher at ISCTE – University Institute of Lisbon
Publications - 17
Citations - 83
Daniel Fernandes is an academic researcher from ISCTE – University Institute of Lisbon. The author has contributed to research in topics: Cloud computing & Cellular network. The author has an hindex of 4, co-authored 17 publications receiving 56 citations. Previous affiliations of Daniel Fernandes include Universidade Lusófona.
Papers
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Journal ArticleDOI
Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
Daniel Fernandes,António Raimundo,Francisco Cercas,Pedro Sebastiao,Rui Dinis,Lucio Studer Ferreira +5 more
TL;DR: A comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI) is presented, which achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.
Proceedings ArticleDOI
Combining Measurements and Propagation Models for Estimation of Coverage in Wireless Networks
Daniel Fernandes,Gabriela Soares,Diogo Clemente,Rodrigo Cortesao,Pedro Sebastiao,Francisco Cercas,Rui Dinis,Lucio Studer Ferreira +7 more
TL;DR: A novel propagation model is proposed which combines drive test measurements, cell reach statistics, antenna radiation patterns, terrain morphologies, and classical theoretical propagation models and fits with DT measurements.
Proceedings ArticleDOI
Combining Drive Tests and Automatically Tuned Propagation Models in the Construction of Path Loss Grids
Daniel Fernandes,Lucio Studer Ferreira,Mohammad Nozari,Pedro Sebastiao,Francisco Cercas,Rui Dinis +5 more
TL;DR: A methodology to build complete path loss grids for a given site is proposed, Starting from available DTs measurements for certain pixels, path loss is estimated for the remaining ones by tuning a propagation model and extrapolating the path loss for neighboring pixels.
Journal ArticleDOI
A Novel Way to Automatically Plan Cellular Networks Supported by Linear Programming and Cloud Computing
Andre Godinho,Daniel Fernandes,Gabriela Soares,Paulo Pina,Pedro J. Sebastião,Américo Correia,Lucio Studer Ferreira +6 more
TL;DR: A quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming, which was successfully integrated in the professional tool Metric, and is currently being used for cellular planning.
Proceedings ArticleDOI
Traffic Forecast in Mobile Networks: Classification System Using Machine Learning
Diogo Clemente,Gabriela Soares,Daniel Fernandes,Rodrigo Cortesao,Pedro Sebastiao,Lucio Studer Ferreira +5 more
TL;DR: This work proposes a methodology to improve the precision of cell traffic forecasting with a machine learning approach, and selected the features and trained a classifier to allocate the cells between predictable and non- predictable, taking into account previous traffic forecast error.