mlsanityData to 7 Oct 2026

Extending Machine Learning Based RF Coverage Predictions to 3D

arXiv 2409.00050 ↗First posted 19 Aug 2024eess.SP cs.CV cs.ITcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 19 Aug 2024.

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2 of 7 references.

References · 2

  1. 2019RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networkscited by 126
  2. 2020An empirical study on using CNNs for fast radio signal predictioncited by 4

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