Extending Machine Learning Based RF Coverage Predictions to 3D

TL;DR

This paper presents advancements in machine learning models for fast, accurate 3D RF coverage predictions in mmWave environments, including improved data processing and arbitrary transmitter height modeling.

Contribution

It introduces methods for 3D RF coverage prediction with arbitrary transmitter heights using machine learning, enhancing simulation speed and accuracy.

Findings

Enhanced training data pre-processing techniques.

Successful implementation of 3D predictions with arbitrary transmitter heights.

Achieved real-time simulation speeds with high accuracy.

Abstract

This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed.