An Integrated Terrain and Clutter Propagation Model for 1.7 GHz and 3.5 GHz Spectrum Sharing

Anderson, Christopher R.

doi:10.1109/tap.2022.3161496

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…LOS probability is likewise important in spectrum-sharing studies, such as the ones conducted within the CEPT and ITU-R. In these studies, adjacent bands are allocated to services operating in the same geographical area, leading to design systems where minimum interference must be provided to the incumbent or protected users while maximizing the number of users simultaneously accessing the same spectrum [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Line-Of-Sight Prediction in Urban Manhattan-Like Environments

Cicco

Prete

Kodra

et al. 2023

2023 17th European Conference on Antennas and Propagation (EuCAP)

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This paper considers the problem of predicting whether or not a transmitter and a receiver are in Line-of-Sight (LOS) condition. While this problem can be easily solved using a digital urban database and applying ray tracing, we consider the scenario in which only few high-level features descriptive of the propagation environment and of the radio link are available. LOS prediction is modelled as a binary classification Machine Learning problem, and a baseline classifier based on Gradient Boosting Decision Trees (GBDT) is proposed. A synthetic raytracing dataset of Manhattan-like topologies is generated for training and testing a GBDT classifier, and its generalization capabilities to both locations and environments unseen at training time are assessed. Results show that the GBDT model achieves good classification performance and provides accurate LOS probability modelling. By estimating feature importance, it can be concluded that the model learned simple decision rules that align with common sense.

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Section: Introductionmentioning

confidence: 99%