Extending Machine Learning Based RF Coverage Predictions to 3D
This work addresses the need for fast and accurate 3D RF coverage predictions in mmWave communications, but it appears incremental as it builds on existing ML methods with improved data pre-processing.
The paper tackles the problem of predicting signal power in mmWave communications environments by extending machine learning models to 3D with arbitrary transmitter height, achieving good accuracy and real-time simulation speeds.
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.