NSF-AoF: Impact of user, environment, and artificial surfaces on above-100 GHz wireless communications

NSF-AoF：用户、环境和人造表面对 100 GHz 以上无线通信的影响

• 批准号: 2133655
• 负责人: Andreas Molisch
• 金额: $ 49万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133655&HistoricalAwards=false
• 关键词: NSF; AoF; Impact; user; environment

Over the past 30 years, wireless data rates have increased continuously, from 10 kbit/s in early digital systems to more than 1 Gbit/s in 5G. This trend will continue for 6G systems, with more than 100 Gbit/s required for new applications such as holographic communications. Such high data rates will require the use of previously unused spectrum with very high carrier frequency, called the Terahertz spectrum, because only in this frequency range is there enough bandwidth to enable the high data rates. A major challenge at these very high frequencies is that coverage is much more spotty, and can be more easily interrupted by small objects, and even by the person holding the wireless device, than at lower frequencies. This project will therefore firstly develop tools to assess the coverage and reliability of such Terahertz systems, and secondly, will create new technology for improving performance, through the development of smart wall coverings that can redirect wireless signals. Together with the appropriate deployment planning tools (e.g., where to put the access point), these developments will constitute a significant step towards enabling ultra-high-data-rate wireless services at lower cost. The project will contribute to workforce development by creating research experiences, involving both theory and experiments, for a diverse team of both undergraduate and graduate students.In this project, several aspects of the 100-500 GHz wireless systems will be covered: (i) The modeling of antenna arrays in portable devices and wave propagation environments under realistic operational conditions; this includes the effects of user hand, head, and body being close to the antennas, which are particularly important at high frequencies; (ii) The design of novel aggregate reflection surfaces and waveguiding surfaces that effectively transfer energy from one device to another in non-line-of-sight (non-LOS) conditions, and the measurement of their performance; (iii) The development of new point-cloud based tools for predicting coverage with dramatically increased accuracy under reasonable runtime -- this is important not only for planning deployment but also to improve the understanding of the nature of wave propagation at these high frequencies; and (iv) Coverage optimization through neural networks and deep learning -- the massive amount of propagation data generated through the point-cloud based tools will be used to train these neural networks, thereby allowing the performance of a particular deployment to be assessed, and the locations of access points and reflective surfaces to be optimized.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.

在过去的30年里，无线数据速率不断增加，从早期数字系统的10kbit /s到5G的1gbit /s以上。这一趋势将持续到6G系统，全息通信等新应用需要超过100 Gbit/s。如此高的数据速率将需要使用以前未使用的具有非常高载波频率的频谱，称为太赫兹频谱，因为只有在这个频率范围内才有足够的带宽来实现高数据速率。在这些非常高的频率下，一个主要的挑战是，与低频率相比，覆盖范围更不稳定，更容易被小物体打断，甚至被拿着无线设备的人打断。因此，该项目将首先开发工具来评估这种太赫兹系统的覆盖范围和可靠性，其次，将通过开发可以重定向无线信号的智能墙壁覆盖物来创造提高性能的新技术。加上适当的部署规划工具（例如，在何处放置接入点），这些发展将构成以较低成本实现超高数据速率无线服务的重要一步。该项目将通过为本科生和研究生的多元化团队创造涉及理论和实验的研究经验，为劳动力发展做出贡献。在这个项目中，将涉及100-500千兆赫无线系统的几个方面：(i)在实际操作条件下便携式设备和波传播环境中天线阵列的建模；这包括用户的手、头和身体靠近天线的影响，这在高频下尤为重要；（ii）设计在非视距条件下有效地将能量从一个装置转移到另一个装置的新型聚合反射面和波导面，并测量其性能；（iii）开发新的基于点云的工具，在合理的运行时间内以大大提高的精度预测覆盖范围——这不仅对规划部署很重要，而且对提高对这些高频波传播性质的理解也很重要；（iv）通过神经网络和深度学习进行覆盖优化——通过基于点云的工具生成的大量传播数据将用于训练这些神经网络，从而允许评估特定部署的性能，并优化接入点和反射面的位置。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。

项目成果

PMNet: Robust Pathloss Map Prediction via Supervised Learning

PMNet：通过监督学习进行稳健的路径损耗图预测

• 发表时间: 2023
• 期刊: Proc. IEEE Globecom
• 作者: Lee, J. H.;Serbetci, O. G.;Selvam, D. P.;Molisch, A. F.
• 通讯作者: Molisch, A. F.

PMNet: Large-Scale Channel Prediction System for ICASSP 2023 First Pathloss Radio Map Prediction Challenge

PMNet：用于 ICASSP 2023 首届路径损耗无线电地图预测挑战赛的大规模信道预测系统

• DOI: 10.1109/icassp49357.2023.10095257
• 发表时间: 2023
• 期刊: IEEE
• 作者: Lee, Ju-Hyung;Lee, Joohan;Lee, Seon-Ho;Molisch, Andreas F.
• 通讯作者: Molisch, Andreas F.

Impact of Blockers on User Equipment Angular Diversity in THz Microcellular Scenarios

• DOI: 10.1109/icc45041.2023.10279799
• 发表时间: 2023-05
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Jorge Gómez-Ponce;Naveed A. Abbasi;S. Abu-Surra;Gary Xu;Charlie Zhang;A. Molisch

A High-resolution Parameter Extraction Algorithm for Multiple Clusters Channels

一种多簇通道高分辨率参数提取算法

• 发表时间: 2022
• 期刊: IEEE SPAWC 2022
• 作者: Zihang Cheng∗, Jorge Gomez-Ponce∗†