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：本文针对阴影与多衰落环境下移动用户的需求挑战，聚焦于基于上行信道估计的下行（DL）无线节点选择问题。本研究的核心议题之一是集成估计信道场景中的预测性能。我们提出了一种自适应深度学习方法以提升性能，为按需空对地链路选择提供了传统插值技术的有效替代方案。此外，本研究考虑了3D信道模型——相较于2D模型，该模型能更真实准确地表征信道特性，尤其适用于3D网络节点分布场景。在应对城市环境中几何随机3D 3GPP信道模型的复杂多径衰落效应时，这一考量至关重要。同时，本研究强调需要构建自适应预测机制，在DL链路预测频响精度与估计/预测的复杂度需求之间实现审慎权衡。通过解决上述问题，本文推动了3D无线电资源管理技术的发展，为动态环境下能量受限的飞行网络节点实现了更高效可靠的通信。