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.

我们的时代目睹了无数新无线应用的出现。示例包括通过连接的车辆进行自动驾驶，高清现场直播，普遍的信息淋浴，物联网（IoT）和地面 - 卫星合作，仅举几例。但是，当今的无线解决方案在支持这些应用程序方面落后。例如，用于实时流媒体的立体高动态范围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功能。带有多吉格赫兹带宽和大量天线阵列的MM波收音机有可能带来许多无线应用程序，从梦想到现实。但是，他们的潜力带来了巨大的挑战。一方面，MM波会经历与当前无线系统不同的复杂且随时间变化的电磁传播效应。另一方面，由于成本考虑，MM波系统设计受到独特的混合数字和模拟结构的影响。该项目为这些前所未有的挑战开发了范式移动解决方案，并在静态和移动方案中铺平了MM波技术广泛部署的道路。预计研究结果将推进最先进的无线技术，并提高混合MM-Wave大量MIMO部署的准备，并与大量的移动用户一起部署。该项目制定了一项综合教育计划，可以为劳动力做准备，以应对无线通信和传感方面的未来挑战。这项研究包括致力于使用混合结构的MM波大量收发器的设计和优化的开创性努力，在具有挑战性的双重选择渠道的传播下，以及可以配备超低复杂性结构的多个用户设备。首先，将通过利用通道的双重稀疏性来开发混合双重选择的MM波大波通道估计器。基于估计的通道，将采用一种固有的宽带方法来设计多用户混合收发器。尽管在混合动力收发器处有很少的射频（RF）链，但这些收发器将进一步优化以利用空间多路复用增益。最重要的是，还将努力解决与超低复杂性混合动力收发器相关的极端系统设计和优化挑战，该挑战与超低 - 深度 - 深度类似于数字的转换器和/或超低位置类似相似的相位相位相变的奖励相关的奖励反映了NSF的法规及其范围的范围，这表明了NSF的法定范围和宽广的影响。 标准。

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