# An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

arXiv: https://arxiv.org/abs/2101.11760 · HTML: https://mlsanity.com/paper/2101.11760/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 93 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015)
- [Recurrent Neural Networks For Accurate RSSI Indoor Localization](https://mlsanity.com/paper/1903.11703/) (2019)
- [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems](https://mlsanity.com/paper/1804.05347/) (2018)
- [Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization](https://mlsanity.com/paper/2008.07111/) (2020)

## Built on by (most cited first, 8 of 8)

- [RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments](https://mlsanity.com/paper/2406.16907/) (2024)
- [COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling](https://mlsanity.com/paper/2411.11798/) (2024)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems](https://mlsanity.com/paper/2409.01315/) (2024)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction](https://mlsanity.com/paper/2507.04595/) (2025)
- [Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.24819/) (2025)
- [A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN](https://mlsanity.com/paper/2608.11083/) (2026)

## Abstract (arXiv)

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with machine learning-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area.