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

NSF-AoF：CNS 核心：小型：空中：毫米波空对地通道建模和跟踪

• 批准号: 2133662
• 负责人: Sundeep Rangan
• 金额: $ 46.4万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133662&HistoricalAwards=false
• 关键词: NSF; AoF; CNS; Core; Small

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.

无人机（UAV）在许多新兴行业中变得不可或缺。该技术的爆炸性增长导致了大量新应用的产生，这些新应用提出了严格的无线通信性能要求。在最短的时间内交换大量的传感数据在大多数操作中至关重要，例如监视，灾难响应监视和搜索和救援。因此，毫米波（mmWave）频段中的大量可用频谱代表了未来空中无线通信的关键技术。在这一领域，有几个基本问题仍然没有答案。特别是，表征毫米波信号在半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未决问题。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。该项目的目标是双重的：（i）进行真实世界的无人机毫米波信道测量，以获得准确的信道模型和（ii）设计旨在最大限度地提高无人机连接的信道跟踪算法。该项目的目标是推进无人机毫米波信道传播的理解，并相应地设计新颖的定向信道跟踪算法，以改善无人机毫米波连接。为此，该团队计划：（1）开发一种定制的基于无人机的毫米波信道探测器，带有波束可调相控阵天线，以实现针对无人机优化的信道测量;（2）使用（1）中开发的信道探测器进行广泛的A2 G和A2 A毫米波测量;（3）训练生成神经网络以导出真实的空中毫米波信道模型，并将它们与现有的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，以改善无人机毫米波连接。该团队所采用的方法将加速要求高数据速率和低延迟的多种无人机应用的创新。该奖项反映了NSF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。

项目成果

Power-Efficient Beam Tracking During Connected Mode DRX in mmWave and Sub-THz Systems

• DOI: 10.1109/jsac.2021.3071791
• 发表时间: 2020-10
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: S. Shah;Sundar Aditya;S. Rangan

Multi-cell Multi-beam Prediction using Auto-encoder LSTM for mmWave systems

• DOI: 10.1109/twc.2022.3183632
• 发表时间: 2021-12
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Syed Hashim Ali Shah;S. Rangan

Parametrization of High-Rank Line-of-Sight MIMO Channels with Reflected Paths

具有反射路径的高阶视距 MIMO 信道的参数化

• DOI: 10.1109/spawc51304.2022.9833962
• 发表时间: 2022
• 期刊: IEEE SPAWC
• 作者: Hu, Yaqi;Yin, Mingsheng;Rangan, Sundeep;Mezzavilla, Marco
• 通讯作者: Mezzavilla, Marco

Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification

• DOI: 10.1109/ojcoms.2022.3155572
• 发表时间: 2021-10
• 期刊: IEEE Open Journal of the Communications Society
• 影响因子: 7.9
• 作者: Mingsheng Yin;A. Veldanda;Amee Trivedi;Jeff Zhang;K. Pfeiffer;Yaqi Hu;S. Garg;E. Erkip;L. Righetti;S. Rangan

135GHz CMOS / LTCC MIMO Receiver Array Tile Modules

135GHz CMOS / LTCC MIMO 接收器阵列块模块

• DOI: 10.1109/bcicts50416.2021.9682493
• 发表时间: 2021
• 期刊: 10.1109/BCICTS50416.2021.9682493
• 作者: Farid, Ali A.;Ahmed, A. S.;Dhananjay, A;Skrimponis, Panagiotis;Rangan, Sundeep;Rodwell, Mark
• 通讯作者: Rodwell, Mark