Future denser air-ground vehicle networks (AGVNs) face challenges such as resource allocation, mobility management, secure transmission, and so on. At the same time, surveillance is a must for modern air traffic management. This motivates us to find opportunities in the aerial vertical by forming a conceptual surveillance plane for aerial vehicles. In this article, we propose an enhanced software-defined network architecture where the surveillance plane can provide local and global surveillance information to macro stations, acting as a side system for the communication links. We review air- ground communications and, by summarizing challenges and opportunities, propose the enhanced architecture of side-information-assisted networks in detail. We then present how we obtain, organize, manage, and utilize the local and global side information by a so-called aviation data lake (ADL). The data lake can be easily connected with advanced machine learning schemes and, thus, provide timely, context-aware metrics and predictions.

中文翻译：

---

航空数据湖：使用辅助信息来增强未来的空中地面车辆网络

未来的更密集的空地车辆网络（AGVN）面临诸如资源分配，移动性管理，安全传输等挑战。同时，监视是现代空中交通管理的必备条件。这激发了我们通过形成用于飞行器的概念性侦察机在空中垂直方向寻找机会的动机。在本文中，我们提出了一种增强的软件定义的网络体系结构，其中监视平面可以向宏站提供本地和全局监视信息，从而充当通信链路的辅助系统。我们回顾了空中通信，并通过总结挑战和机遇，详细提出了辅助信息辅助网络的增强架构。然后，我们介绍如何获取，组织，管理，并通过所谓的航空数据湖（ADL）利用本地和全球辅助信息。数据湖可以轻松地与高级机器学习方案连接，从而提供及时的上下文感知指标和预测。