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Investigating automated hyper-parameter optimization for a generalized path loss model
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Abstract:
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 optimization, three surrogate models (the
Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were
considered. To the best of our knowledge, few works have been found on automatic
hyper-parameter optimization for path loss prediction and none of the works used
the aforementioned optimization techniques. Differentiation among the various
environments was achieved by the assignment of the clutter height values based on
International Telecommunication Union Recommendation (ITU-R) P.452-16. We
also included the elevation of the transmitting antenna position as a feature so as to
capture its effect on path loss. The best machine learning model observed is K
Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2),
average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE)
values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100
different samplings of train set and test set. Results show that machine learning can
also be used to develop path loss models that are valid for a certain range of distances,
frequencies, antenna heights, and environment types. HyperBand produced hyperparameter
configurations with the highest accuracy in most of the algorithms.
Description:
Date:
2021
Authors:
Usman Sammani Sani
Lai, Daphne Teck Ching
Owais Ahmed Malik
Publisher:
IOS Press
