André Godinho

Mr. André Godinho

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Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 April 2020 in Applied Sciences
Verified
Journal Article
Open access
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AGAndré Godinho
André Godinho
DFDaniel Fernandes
Daniel Fernandes
GSGabriela Eleutério Soares
Gabriela Eleutério Soares
PPPaulo Pina
Paulo Pina
PSPedro Sebastiao
Pedro Sebastiao
Lucio Studer FerreiraLucio Studer Ferreira
Lucio Studer Ferreira
With the increasing number of mobile subscribers worldwide, there is a need for fast and reliable algorithms for planning/optimization of mobile networks, especially because, in order to maintain a network’s quality of service, an operator might need to deploy more equipment. This paper presents a quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming. We evaluate our pattern in a realistic scenario of a Global System for Mobile communications protocol (GSM) network and compare the results to another already implemented commercial tool. Results show that even though network quality was similar, our algorithm was twelve times faster and used four times less memory. It was also able to frequency plan seventy cells simultaneously in less than three minutes. This mechanism was successfully integrated in the professional tool Metric, and is currently being used for cellular planning. Its extension for application to 3/4/5G networks is under study.

9 April 2020 in IEEE Access
Verified
Journal Article
Open access
PPPaulo M. Pina
Paulo M. Pina
AGAndré Godinho
André Godinho
DFDaniel Fernandes
Daniel Fernandes
DCDiogo J. A. Clemente
Diogo J. A. Clemente
PSPedro Sebastiao
Pedro Sebastiao
Lucio Studer FerreiraLucio Studer Ferreira
Lucio Studer Ferreira
Over the years, mobile networks have grown exponentially due to rising demand. These networks mix different types of cells, which makes manual configuration difficult, costly and tedious. Furthermore, inefficiencies stemming from these problems can cause problems in Handover performance, since a mobile device may not always connect to the optimal cell upon switching from one to the other, potentially harming service quality and increasing Operational Costs (OPEX). Existing solutions, like Automatic Neighbour Relations (ANR), while they are valuable in estimating the best neighbouring cells through the rate of successful Handovers, fail to take into account topological coverage factors and fictional cells, therefore inefficiencies lie hidden and it isn’t suited to calculate relations between planned cells and active cells. In this article, a proposal of a cloud-based, on-demand automatic coverage based neighbour estimation system is proposed, which utilises topological signal coverage data from each cell provided by the network’s operations support system, in order to mitigate the aforementioned issues and provide a reliable and convenient coverage analysis paradigm.