A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 6; 2026: 1.
11 papers.
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 27
- 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networkscited by 8
- 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learningcited by 3
- 2025MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networkscited by 3
- 2023FLSTRA: Federated Learning in Stratospherecited by 1
- 2025MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attentioncited by 1
- 2025Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRLcited by 0
- 2025PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modellingcited by 0
- 2025MobiAct: Efficient MAV Action Recognition Using MobileNetV4 with Contrastive Learning and Knowledge Distillationcited by 0
- 2025Wavefront-Constrained Passive Obscured Object Detectioncited by 0
- 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0
- 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks10 May 2026
- 2025Wavefront-Constrained Passive Obscured Object Detection26 Nov 2025
- 2025MobiAct: Efficient MAV Action Recognition Using MobileNetV4 with Contrastive Learning and Knowledge Distillation22 Oct 2025
- 2025MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention17 Oct 2025
- 2025MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks16 Oct 2025
- 2025PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling7 Oct 2025
- 2025Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRL3 Apr 2025
- 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning5 Jun 2023
- 2023FLSTRA: Federated Learning in Stratosphere1 Feb 2023
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey7 Nov 2022
- 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks2 Feb 2021