Collaborative Research: Beamforming, User Association and Precise Positioning in Future Terahertz-Enabled Wireless Networks

合作研究：未来太赫兹无线网络中的波束成形、用户关联和精确定位

• 批准号: 2234122
• 负责人: Mai Vu
• 金额: $ 24.48万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234122&HistoricalAwards=false
• 关键词: Collaborative; Research; Beamforming; User; Association

The wireless industry is entering an unprecedented era characterized by orders of magnitude increase in both carrier frequency and channel bandwidth. Key features including wide channel bandwidths, adaptable directional antennas, and dense cell deployments will set future wireless systems on a rapid expansion of capabilities. While mmWave frequencies offer improved wireless data rates, the growing demand for emerging applications like wireless cognition, holographic communications, virtual/augmented reality, autonomous driving, and wireless backhaul necessitates even higher data rates beyond what 5G networks can provide. To achieve data rates of around 100 Gbps or more, it becomes essential to explore frequencies in the sub-THz and THz range, surpassing 100 GHz. These frequency ranges offer abundant available spectra, enabling wider bandwidth radio frequency (RF) channels and the next leap in data rate capabilities. This project aims to leverage the potential of sub-THz frequencies by addressing fundamental challenges in extremely wideband, highly directional channels.Specifically, the project focuses on the design of hybrid beamforming for a wideband multicarrier system in the presence of beam squinting caused by the extremely wide bandwidth. It also investigates joint user association with base stations and reconfigurable intelligent surfaces (RISs) within the dense and directional THz environment. Moreover, the project explores the use of THz-enabled centimeter-accuracy positioning capabilities, enabling more precise and agile beamforming, as well as enhanced association and handover mechanisms for mobile devices. To accomplish these goals, optimization and deep learning techniques will be employed in algorithm design. Furthermore, the project will involve using measurements of THz signals obtained by a channel sounder to emulate virtual transmitter and receiver locations in real-world environments. These measurements will assist in calibrating the NYU ray tracer and provide valuable data for algorithms evaluation. The use of RISs and the Precision Time Protocol over wireless networks will facilitate real-time, THz-based precise position-location capabilities. Incorporating actual sub-THz channel measurements and the position location information of transmitters, receivers, and RISs will provide additional insights to enhance the performance of the designed algorithms.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.

无线产业正在进入一个前所未有的时代，其特征在于载波频率和信道带宽都有数量级的增加。包括宽信道带宽、可适应的定向天线和密集小区部署在内的关键特性将使未来的无线系统的能力迅速扩展。虽然毫米波频率提供了更高的无线数据速率，但对无线认知、全息通信、虚拟/增强现实、自动驾驶和无线回程等新兴应用的需求不断增长，需要比5G网络更高的数据速率。为了实现约100 Gbps或更高的数据速率，必须探索sub-THz和THz范围内的频率，超过100 GHz。这些频率范围提供了丰富的可用频谱，实现了更宽带宽的射频（RF）信道和数据速率能力的下一次飞跃。该项目旨在通过解决极宽带、高方向性信道中的基本挑战来充分利用亚太赫兹频率的潜力。具体而言，该项目侧重于在极宽带宽导致的波束偏斜存在的情况下，宽带多载波系统的混合波束形成设计。它还研究了密集和定向THz环境中与基站和可重构智能表面（RIS）的联合用户关联。此外，该项目还探索了使用太赫兹功能的厘米精度定位功能，实现更精确和灵活的波束成形，以及增强移动的设备的关联和切换机制。为了实现这些目标，将在算法设计中采用优化和深度学习技术。此外，该项目将涉及使用信道探测器获得的THz信号测量值来模拟真实环境中的虚拟发射器和接收器位置。这些测量将有助于校准纽约大学射线示踪剂，并为算法评估提供有价值的数据。在无线网络上使用RISs和精确时间协议将促进基于太赫兹的实时精确定位能力。验证实际的亚太赫兹信道测量和发射机、接收机和RISs的位置定位信息将为提高设计算法的性能提供额外的见解。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。