# Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models

arXiv: https://arxiv.org/abs/2408.12193 · HTML: https://mlsanity.com/paper/2408.12193/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach](https://mlsanity.com/paper/2106.12556/) (2021)
- [Learning-Based Multi-Channel Access in 5G and Beyond Networks with Fast Time-Varying Channels](https://mlsanity.com/paper/2003.14403/) (2020)

## Built on by (most cited first, 4 of 4)

- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [The First Indoor Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2501.13698/) (2025)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)

## Abstract (arXiv)

The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications.