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Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model
[chapter]
2021
Frontiers in Artificial Intelligence and Applications
This work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian
doi:10.3233/faia210413
fatcat:6xa6yqcj4jhndg7bwmkpg7cqzu