# A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/1801.01656 · HTML: https://mlsanity.com/paper/1801.01656/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 11 of 11)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks](https://mlsanity.com/paper/2510.14770/) (2025)
- [FLSTRA: Federated Learning in Stratosphere](https://mlsanity.com/paper/2302.00163/) (2023)
- [MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention](https://mlsanity.com/paper/2510.15448/) (2025)
- [Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRL](https://mlsanity.com/paper/2504.02688/) (2025)
- [PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling](https://mlsanity.com/paper/2510.06355/) (2025)
- [MobiAct: Efficient MAV Action Recognition Using MobileNetV4 with Contrastive Learning and Knowledge Distillation](https://mlsanity.com/paper/2510.19273/) (2025)
- [Wavefront-Constrained Passive Obscured Object Detection](https://mlsanity.com/paper/2511.20991/) (2025)
- [Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks](https://mlsanity.com/paper/2605.09813/) (2026)

## Abstract (arXiv)

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.