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http://hdl.handle.net/10071/20683
acessibilidade
Title: Comparison of artificial intelligence and semi-empirical methodologies for estimation of coverage in mobile networks
Authors: Fernandes, D.
Raimundo, A.
Cercas, F.
Sebastião, P.
Dinis, R.
Ferreira, L. S.
Keywords: Coverage estimation
Network planning
Drive tests
Measurements
Propagation model
Artificial intelligence
Issue Date: 2020
Publisher: IEEE
Abstract: To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this article presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6.1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0.5 dB, since this only achieves a MAE up to 6.6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/20683
DOI: 10.1109/ACCESS.2020.3013036
ISSN: 2169-3536
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-73904
Appears in Collections:IT-RI - Artigo em revista internacional com arbitragem científica

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