# Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication

arXiv: https://arxiv.org/abs/2012.09133 · HTML: https://mlsanity.com/paper/2012.09133/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

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

- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [How Generative Models Improve LOS Estimation in 6G Non-Terrestrial Networks](https://mlsanity.com/paper/2305.18845/) (2023)
- [Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing](https://mlsanity.com/paper/2110.08952/) (2021)
- [A Federated Channel Modeling System using Generative Neural Networks](https://mlsanity.com/paper/2305.18856/) (2023)
- [Traffic Simulation in Ad Hoc Network of Flying UAVs with Generative AI Adaptation](https://mlsanity.com/paper/2602.13200/) (2026)
- [Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility](https://mlsanity.com/paper/2606.20098/) (2026)

## Abstract (arXiv)

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for 28 GHz air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.