NSF-AoF: CNS Core: Small: AERIAL: Air-to-Ground Channel Modeling and Tracking at Millimeter-Wave

无人驾驶飞行器（uav）在许多新兴产业中变得不可或缺。该技术的爆炸性扩散导致了大量新应用的产生，这些应用对无线通信性能提出了严格的要求。在最短的时间内交换大量的传感数据对于大多数操作（如监视、灾难响应监测和搜索和救援）至关重要。因此，毫米波（mmWave）频段的大量可用频谱代表了未来空中无线通信的关键使能技术。在这个领域，几个基本问题仍未得到解答。特别是，表征毫米波信号在空中的传播以及设计信道跟踪机制以最大化空中连接仍然是重要的开放性问题。信道模型是设计无线系统的关键组成部分。然而，全球蜂窝标准化机构依赖于基于地面测量的毫米波信道模型。该项目的目标有两个：(i)进行真实世界的无人机毫米波通道测量，以获得准确的通道模型；（ii）设计旨在最大化无人机连接的通道跟踪算法。该项目的目标是提高对无人机毫米波信道传播的理解，并相应地设计新的定向信道跟踪算法，以改善无人机毫米波连通性。为此，该团队计划：(1)开发一种基于无人机的定制毫米波信道测深仪，该测深仪带有波束可操纵相控阵天线，以实现针对无人机进行优化的信道测量；(2)使用在(1)中开发的通道测深仪进行广泛的A2G和A2A毫米波测量；(3)训练生成式神经网络，推导出真实的航空毫米波信道模型，并与现有的3GPP模型进行比较；(4)开发基于人工智能的空中信道跟踪算法，提高无人机毫米波连通性。该团队采用的方法将加速需要高数据速率和低延迟的众多无人机应用的创新。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Unmanned Aerial Vehicles (UAVs) are becoming indispensable in many emerging industries. The explosive proliferation of this technology has resulted in the creation of a plethora of new applications that pose stringent wireless communications performance requirements. Exchanging large volumes of sensory data in the minimum amount of time is critical in most operations such as surveillance, disaster response monitoring, and search-and-rescue. For this reason, the massive amount of available spectrum in the millimeter-wave (mmWave) bands represents a key enabling technology for future aerial wireless communications. In this realm, several fundamental questions remain unanswered. In particular, characterizing the mmWave signal propagation in mid-air as well as designing the channel tracking mechanisms to maximize aerial connectivity remain important open questions. Channel models represent a key building block to design wireless systems. However, global cellular standardization bodies rely on mmWave channel models that are based on terrestrial measurements. The goal of this project is twofold: (i) Conduct real-world UAV mmWave channel measurements to derive accurate channel models and (ii) design channel tracking algorithms that are aimed at maximizing UAV connectivity. The goal of this project is to advance the understanding of UAV mmWave channel propagation and, accordingly, design novel directional channel tracking algorithms to improve UAV mmWave connectivity. To do so, the team plans plan to: (1) Develop a custom UAV-based mmWave channel sounder with beam steerable phased array antenna to enable channel measurements that are optimized for UAVs; (2) Conduct extensive A2G and A2A mmWave measurements using the channel sounder developed in (1); (3) Train generative neural networks to derive realistic aerial mmWave channel models and compare them with existing 3GPP models; (4) Develop AI-based aerial channel-tracking algorithms to improve UAV mmWave connectivity. The approach adopted by the team will accelerate innovation in a multitude of UAV applications that demand high data rates and low latency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.