Collaborative Research: NSF-AoF: CIF: AF: Small: Energy-Efficient THz Communications Across Massive Dimensions

合作研究：NSF-AoF：CIF：AF：小型：大尺寸的节能太赫兹通信

• 批准号: 2225575
• 负责人: Arnold Swindlehurst
• 金额: $ 30万
• 依托单位: University of California-Irvine
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225575&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; AoF; CIF

Due to the limited spectrum available in the frequency bands currently used for cellular communications, beyond-5G wireless systems are expected to exploit the large amount of bandwidth available in the THz band (0.3-3 THz). This will require the use of very large arrays of antennas at both the transmitter and the receiver to compensate for the strong attenuation and lack of penetration of signals at these high frequencies. However, scaling up existing radio-frequency technologies to operate over large bandwidths becomes exceedingly complex, expensive, and demands high power consumption. Thus, radical simplifications in the radio-frequency architecture are needed; for example, a sacrifice in the resolution of the data converters will be inevitable when operating across massive frequency and antenna dimensions. This collaborative project tackles the theoretical and practical challenges associated with using very large arrays with digital receivers attached to each antenna and that employ low-resolution analog-to-digital/digital-to-analog converters. It aims to realize this technology's potential for revolutionizing the physical layer in THz communications. The project adopts a holistic approach that encompasses analytical studies, signal processing methods, and network protocols and leverages rigorous tools from optimization, machine learning, and Bayesian inference. Furthermore, it will foster the research collaboration between the participating US and Finnish institutions and support the cross-disciplinary development of a diverse cohort of Ph.D., Master's, and undergraduate students in key technologies for beyond-5G/6G systems.This project will address the fundamental physical-layer challenges associated with energy-efficient THz communications in beyond-5G systems and is organized into four interconnected thrusts: i) Doubly massive multi-input multi-output (MIMO) systems with low-resolution ADCs/DACs at both ends of the link, focusing on the channel estimation, performance analysis, (symbol-level) precoding and decoding design, and hardware non-linearities; ii) Spatial Sigma-Delta processing under realistic array and radio-frequency models, incorporating two-dimensional spatial sampling and carefully characterizing the mutual antenna coupling and out-of-band emissions; iii) Initial access protocols to facilitate the practical implementation of fully digital architectures, focusing on synchronization signal design and signal-to-noise ratio tuning schemes; and iv) Variational Bayesian inference applied to channel estimation and data detection with low-resolution ADCs, to be used in lieu of traditional machine learning models in rapidly time-varying environments. By demonstrating the potential and feasibility of pushing the operating frequencies to the THz realm, the project will stimulate cross-disciplinary research efforts, encourage technological advancements in low-complexity and controllable antenna architectures, and enable new wireless applications with high data rates and low latencies.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.

由于目前用于蜂窝通信的频带中可用的频谱有限，预期5G以上无线系统将利用THz频带（0.3-3 THz）中可用的大量带宽。这将需要在发射机和接收机处使用非常大的天线阵列，以补偿这些高频信号的强衰减和穿透不足。然而，将现有的射频技术按比例放大以在大带宽上操作变得极其复杂、昂贵并且需要高功耗。因此，需要对射频架构进行彻底的简化;例如，当在大规模频率和天线尺寸上工作时，牺牲数据转换器的分辨率将是不可避免的。这个合作项目解决了与使用非常大的阵列与数字接收器连接到每个天线，并采用低分辨率模数/数模转换器相关的理论和实践挑战。它的目的是实现这种技术的潜力，彻底改变太赫兹通信的物理层。该项目采用整体方法，包括分析研究，信号处理方法和网络协议，并利用优化，机器学习和贝叶斯推理的严格工具。此外，它将促进参与的美国和芬兰机构之间的研究合作，并支持跨学科发展的博士学位，硕士和本科生在超越5G/6 G系统的关键技术。该项目将解决与超越5G系统中的节能THz通信相关的基本物理层挑战，并分为四个相互关联的重点：i）在链路两端具有低分辨率ADC/DAC的双大规模多输入多输出（MIMO）系统，重点是信道估计、性能分析，（符号级）预编码和解码设计，以及硬件非线性; ii）在现实阵列和射频模型下的空间Σ-Δ处理，结合二维空间采样并仔细表征相互天线耦合和带外发射; iii）初始访问协议，以促进全数字架构的实际实施，重点是同步信号设计和信噪比调谐方案;以及iv）应用于低分辨率ADC的信道估计和数据检测的变分贝叶斯推理，以在快速时变环境中代替传统的机器学习模型。通过展示将工作频率推向太赫兹领域的潜力和可行性，该项目将刺激跨学科研究工作，鼓励低复杂性和可控天线架构的技术进步，该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。

项目成果

A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

• DOI: 10.1109/ieeeconf56349.2022.10052059
• 发表时间: 2022-10
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen

Variational Bayes Inference for Data Detection in Cell-Free Massive MIMO

用于无细胞大规模 MIMO 数据检测的变分贝叶斯推理

• DOI: 10.1109/ieeeconf56349.2022.10051916
• 发表时间: 2022
• 期刊: and Computers
• 作者: Nguyen, Ly V.;Ngo, Hien Quoc;Tran, Le-Nam;Swindlehurst, A. Lee;Nguyen, Duy H.
• 通讯作者: Nguyen, Duy H.

Deep Unfolding-Enabled Hybrid Beamforming Design for mmWave Massive MIMO Systems

• DOI: 10.1109/icassp49357.2023.10096658
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: N. Nguyen;Mengyuan Ma;Nir Shlezinger;Y. Eldar;A. L. Swindlehurst;M. Juntti

Overlay Cognitive Radio Using Symbol Level Precoding With Quantized CSI

使用带有量化 CSI 的符号级预编码的叠加认知无线电

• DOI: 10.1109/icassp49357.2023.10095726
• 发表时间: 2023
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Liu, Lu;Swindlehurst, A. Lee
• 通讯作者: Swindlehurst, A. Lee

Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

• DOI: 10.1109/twc.2022.3193885
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Ly V. Nguyen;D. Nguyen;A. L. Swindlehurst