This paper addresses the challenges of mobile user requirements in shadowing and multi-fading environments, focusing on the Downlink (DL) radio node selection based on Uplink (UL) channel estimation. One of the key issues tackled in this research is the prediction performance in scenarios where estimated channels are integrated. An adaptive deep learning approach is proposed to improve performance, offering a compelling alternative to traditional interpolation techniques for air-to-ground link selection on demand. Moreover, our study considers a 3D channel model, which provides a more realistic and accurate representation than 2D models, particularly in the context of 3D network node distributions. This consideration becomes crucial in addressing the complex multipath fading effects within geometric stochastic 3D 3GPP channel models in urban environments. Furthermore, our research emphasises the need for adaptive prediction mechanisms that carefully balance the trade-off between DL link forecasted frequency response accuracy and the complexity requirements associated with estimation and prediction. This paper contributes to advancing 3D radio resource management by addressing these challenges, enabling more efficient and reliable communication for energy-constrained flying network nodes in dynamic environments.

翻译：本文针对阴影衰落与多径衰落环境下移动用户的需求挑战，重点研究基于上行链路信道估计的下行链路无线节点选择问题。本研究的核心课题之一是解决信道估计集成场景下的预测性能问题。我们提出了一种自适应深度学习方法以提升预测性能，为按需空对地链路选择提供了传统插值技术之外的优异替代方案。此外，本研究采用三维信道模型——相较于二维模型，该模型能更真实精确地反映三维网络节点分布特性，特别是在城市环境中几何随机三维3GPP信道模型下处理复杂多径衰落效应时尤为关键。研究进一步强调需要建立自适应预测机制，在保证下行链路频响预测精度与处理估计/预测复杂度要求之间取得审慎平衡。通过攻克上述技术挑战，本文为推动三维无线资源管理发展、实现动态环境中能量受限飞行网络节点的高效可靠通信提供了重要支撑。