# Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

arXiv: https://arxiv.org/abs/2102.01751 · HTML: https://mlsanity.com/paper/2102.01751/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets](https://mlsanity.com/paper/1811.03850/) (2018)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Distributed Optimization with Arbitrary Local Solvers](https://mlsanity.com/paper/1512.04039/) (2015)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1801.01656/) (2018)
- [Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication](https://mlsanity.com/paper/2012.09133/) (2020)
- [Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things](https://mlsanity.com/paper/1906.00567/) (2019)
- [Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets](https://mlsanity.com/paper/2002.00306/) (2020)
- [Millimeter Wave Channel Modeling via Generative Neural Networks](https://mlsanity.com/paper/2008.11006/) (2020)
- [Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors](https://mlsanity.com/paper/1908.03271/) (2019)
- [An Experimental mmWave Channel Model for UAV-to-UAV Communications](https://mlsanity.com/paper/2007.11869/) (2020)
- [Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning](https://mlsanity.com/paper/2002.10572/) (2020)
- [On Optimal Multi-user Beam Alignment in Millimeter Wave Wireless Systems](https://mlsanity.com/paper/2001.06595/) (2020)
- [Temporal and Spatial Characteristics of mmWave Propagation Channels for UAVs](https://mlsanity.com/paper/1803.08252/) (2018)
- [Analytical Channel Models for Millimeter Wave UAV Networks under Hovering Fluctuations](https://mlsanity.com/paper/1905.01477/) (2019)

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

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [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)
- [Large Generative Model Assisted 3D Semantic Communication](https://mlsanity.com/paper/2403.05783/) (2024)
- [Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks](https://mlsanity.com/paper/2108.08241/) (2021)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Towards Federated Learning-Enabled Visible Light Communication in 6G Systems](https://mlsanity.com/paper/2110.03319/) (2021)
- [A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid](https://mlsanity.com/paper/2403.06388/) (2024)

## Abstract (arXiv)

In this paper, a novel framework is proposed to perform data-driven air-to-ground (A2G) channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. First, an effective channel estimation approach is developed to collect mmWave channel information, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. Next, in order to expand the application scenarios of the trained channel model into a broader spatial-temporal domain, a cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution in a fully-distributed manner. To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure. Simulation results show that the proposed distributed CGAN approach is robust to the local training error at each UAV. Meanwhile, a larger airborne network size requires more communication resources per UAV to guarantee an efficient learning rate. The results also show that, compared with a stand-alone CGAN without information sharing and two other distributed schemes, namely: A multi-discriminator CGAN and a federated CGAN method, the proposed distributed CGAN approach yields a higher modeling accuracy while learning the environment, and it achieves a larger average data rate in the online performance of UAV downlink mmWave communications.