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.
Highlights
The advances of mobile networks technology have made possible to provide mobile devices with new features for its users
COMPARISON the results for the metrics presented in Section II-C are discussed for each of the scenarios presented in Section II-B when the Artificial Intelligence Propagation Model (AIPM) and Automatically Calibrated Standard Propagation Model (ACSPM) models presented in Section III are applied
It is understandable that AIPM needs large data sets to provide realistic results, as a semi-empirical propagation model integrates physical aspects that are realistic. These results indicate that, by increasing the number of Drive Tests (DT) used in the ACSPM calibration, the signal estimation for that antenna improves
Summary
The advances of mobile networks technology have made possible to provide mobile devices with new features for its users. Of all generations of telecommunications, 4G was the most innovative one, since it provided multimedia streaming, which had several limitations on past generations. Be covered by this technology, while 65% has already access to the newer 5G technology [1]. One of the main concerns of telecommunication operators is the Quality of Service (QoS), which measures the overall performance of operators’ services provided to their clients. These cellular networks are constituted by a deployment of antennas, each covering a service area around it, the so-called cell.
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