The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions

翻译：本文提出了一种数据驱动的无人机毫米波无线网络空地信道估计方法。与传统的局限于特定地理区域且不适用于其他区域的集中式学习方法不同，本文提出了一种基于联邦学习（FL）的广义信道估计模型，能够预测低空平台与地面终端之间的空地路径损耗。为此，本文设计的基于联邦学习的生成对抗网络（FL-GAN）作为一种生成式数据模型，无需在训练阶段进行先验数据分析，即可学习不同类型的数据分布，并生成来自相同分布的逼真模式。为评估所提模型的有效性，我们使用合成数据分布与实际数据分布之间的Kullback-Leibler散度（KL）和Wasserstein距离进行性能评估。此外，还将该技术与其他生成模型（如联邦变分自编码器（FL-VAE）以及独立的VAE和GAN模型）进行了对比。研究结果表明，FL-GAN生成的合成数据与真实数据的分布相似度最高，这证明了所提方法在不同区域生成基于数据的信道模型的有效性。