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.

在许多新兴行业中，无人驾驶汽车（UAV）变得必不可少。该技术的爆炸性扩散导致创建了许多新应用程序，这些应用程序构成了严格的无线通信性能要求。在大多数操作中，例如监视，灾难响应监测和搜索搜索，在最短时间内交换大量的感官数据至关重要。因此，在毫米波（MMWave）频段中，大量可用频谱代表了未来空中无线通信的关键技术。在这个领域中，几个基本问​​题仍未解决。特别是，表征空气中间的MMWave信号传播以及设计通道跟踪机制以最大化航空连接仍然是重要的开放问题。频道模型代表设计无线系统的关键构建块。但是，全球细胞标准化机构依赖于基于陆地测量的MMWave通道模型。该项目的目标是双重的：（i）进行现实世界无人机MMWave通道测量值以得出准确的通道模型，并且（ii）设计通道跟踪算法，旨在最大化无人机连接。该项目的目的是提高对无人机MMWave通道传播的理解，并因此设计新颖的方向通道跟踪算法以提高无人机MMWave连接性。为此，团队计划计划：（1）使用带有光束的可划分的分阶段阵列天线开发基于自定义的MMWave频道声音，以启用针对无人机进行优化的通道测量； （2）使用（1）中开发的通道音频进行广泛的A2G和A2A MMWAVE测量； （3）火车生成的神经网络可得出现实的空中MMWAVE频道模型，并将其与现有的3GPP模型进行比较； （4）开发基于AI的航空通道跟踪算法，以提高无人机MMWave连接性。团队采用的方法将在许多无人机应用程序中加速创新，这些应用程序需要高数据速率和低潜伏期。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。

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

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

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

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