mlsanityData to 7 Oct 2026

Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks

arXiv 2411.17752 ↗First posted 25 Nov 2024eess.SP cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 25 Nov 2024.

Builds on

6 of 21 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 302
  4. 2021How to avoid machine learning pitfalls: a guide for academic researcherscited by 34
  5. 2024Machine Learning-Based Path Loss Modeling with Simplified Featurescited by 9
  6. 2022Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurementscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Predictioncited by 0
  2. 2025Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Predictioncited by 0
  3. 2026PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splattingcited by 0