Hybrid mmWave mMIMO Transceiver Design for Doubly-Selective Channels

适用于双选通道的混合毫米波 mMIMO 收发器设计

• 批准号: 2102312
• 负责人: Georgios Giannakis
• 金额: $ 34.45万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102312&HistoricalAwards=false
• 关键词: Hybrid; mmWave; mMIMO; Transceiver; Design

Our era is witnessing the emergence of innumerable new wireless applications. Examples include autonomous driving via connected vehicles, high-definition live streaming, pervasive information showers, internet of things (IoT), and terrestrial-satellite cooperation, just to name a few. However, today's wireless solutions are lagging behind in supporting these applications. For example, the stereoscopic high-dynamic range 360-degree video for live streaming requires 2-20 Gbps data rate. This far exceeds the capability of our current wireless networks. mm-wave radios with multi-gigahertz bandwidth and massive antenna arrays have the potential to bring many wireless applications from dream to reality. However, their potentials come with formidable challenges. On the one hand, mm-wave experiences complicated and time-varying electromagnetic propagation effects that are distinct from current wireless systems. On the other hand, the mm-wave system design is subject to the unique hybrid digital and analog structures due to cost considerations. This project develops paradigm-shifting solutions to these unprecedented challenges and paves the road towards widespread deployment of mm-wave technology in both static and mobile scenarios. The research outcomes are expected to advance the state-of-the-art wireless technologies and improve the readiness of hybrid mm-wave massive MIMO deployment with a massive number of mobile users. The project has an integrated education plan to prepare workforce for future challenges in wireless communications and sensing. The research consists of pioneering efforts dedicated to the design and optimization of mm-wave massive MIMO transceivers with a hybrid structure, under challenging doubly-selective channel propagation, and with multiple user devices that may be equipped with ultra-low-complexity structures. First, a hybrid doubly-selective mm-wave massive MIMO channel estimator will be developed by exploiting the double sparsity of the channel. Based on the estimated channel, an inherently wideband approach will be adopted to design the multi-user hybrid transceivers. These transceivers will be further optimized to exploit the spatial multiplexing gain, despite the very small number of radio-frequency (RF) chains at the hybrid transceivers. On top of all these, efforts will also be made to address extreme system design and optimization challenges associated with ultra-low-complexity hybrid transceivers with ultra-low-bit-depth analog-to-digital converters and/or ultra-low-bit-count analog phase shifter networks.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.

我们的时代正在见证无数新的无线应用的出现。例如，通过联网车辆进行自动驾驶、高清直播、无处不在的信息流、物联网 (IoT) 以及地面卫星合作等等。然而，当今的无线解决方案在支持这些应用方面落后。例如，用于直播的立体高动态范围 360 度视频需要 2-20 Gbps 的数据速率。这远远超出了我们当前无线网络的能力。具有多千兆赫带宽和大规模天线阵列的毫米波无线电有潜力将许多无线应用从梦想变为现实。然而，他们的潜力也伴随着巨大的挑战。一方面，毫米波经历复杂且随时间变化的电磁传播效应，这与当前的无线系统不同。另一方面，出于成本考虑，毫米波系统设计受到独特的数字和模拟混合结构的影响。该项目针对这些前所未有的挑战开发了范式转变的解决方案，并为毫米波技术在静态和移动场景中的广泛部署铺平了道路。研究成果预计将推动最先进的无线技术的发展，并提高针对大量移动用户的混合毫米波大规模 MIMO 部署的准备程度。该项目有一个综合教育计划，旨在帮助员工做好应对无线通信和传感领域未来挑战的准备。该研究包括致力于设计和优化毫米波大规模 MIMO 收发器的开创性工作，该收发器具有混合结构、具有挑战性的双选择信道传播以及可能配备超低复杂度结构的多个用户设备。首先，将利用信道的双稀疏性来开发混合双选择毫米波大规模 MIMO 信道估计器。根据估计的信道，将采用固有的宽带方法来设计多用户混合收发器。尽管混合收发器中的射频 (RF) 链数量非常少，但这些收发器将进一步优化以利用空间复用增益。除此之外，还将努力解决与具有超低位深度模数转换器和/或超低位的超低复杂度混合收发器相关的极端系统设计和优化挑战。 -计数模拟移相器网络。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力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

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients

• DOI: 10.1609/aaai.v37i9.26337
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis

Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network

基于时空图卷积网络的分层交通流预测

• DOI: 10.1109/tits.2022.3148105
• 发表时间: 2022
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Hanqiu Wang;Rongqing Zhang;Xiang Cheng;Liuqing Yang
• 通讯作者: Liuqing Yang

Bayesian Optimization for Task Offloading and Resource Allocation in Mobile Edge Computing

• DOI: 10.1109/ieeeconf56349.2022.10051868
• 发表时间: 2022-10
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: Jiahe Yan;Qin Lu;G. Giannakis

Integrated Distributed Wireless Sensing with Over-The-Air Federated Learning

集成分布式无线传感与空中联合学习

• DOI: 10.1109/igarss52108.2023.10282842
• 发表时间: 2023
• 期刊: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
• 作者: Gao, Shijian;Yan, Jia;Giannakis, Georgios B.
• 通讯作者: Giannakis, Georgios B.

BER-Minimizing Precoded Wideband Generalized Beamspace Modulation for Hybrid mmWave Massive MIMO

用于混合毫米波大规模 MIMO 的 BER 最小化预编码宽带广义波束空间调制

• DOI: 10.1109/lwc.2021.3125778
• 发表时间: 2022
• 期刊: IEEE Wireless Communications Letters
• 影响因子: 6.3
• 作者: Gao, Shijian;Li, Jinpeng;Cheng, Xiang;Yang, Liuqing
• 通讯作者: Yang, Liuqing