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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

基本信息

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

项目摘要

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，通过联网车辆实现的自动驾驶、高清直播、无处不在的信息淋浴、物联网（IoT）和地面卫星合作等等。然而，今天的无线解决方案在支持这些应用方面落后了。例如，用于直播的立体高动态范围360度视频需要2- 20gbps的数据速率。这远远超过了我们目前无线网络的能力。具有数千兆赫带宽和大规模天线阵列的毫米波无线电具有将许多无线应用从梦想变为现实的潜力。然而，他们的潜力也伴随着巨大的挑战。一方面，毫米波的电磁传播效应复杂且时变，这与目前的无线系统不同。另一方面，由于成本的考虑，毫米波系统设计受到独特的数字和模拟混合结构的限制。该项目为这些前所未有的挑战开发了范式转换解决方案，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。该研究成果预计将推动最先进的无线技术，并改善混合毫米波大规模MIMO部署的准备工作，以满足大量移动用户的需求。该项目有一个综合教育计划，为未来无线通信和传感领域的挑战做好准备。该研究包括开创性的工作，致力于设计和优化具有混合结构的毫米波大规模MIMO收发器，在具有挑战性的双选择信道传播下，以及可能配备超低复杂度结构的多用户设备。首先，利用信道的双稀疏性，开发了一种混合双选择性毫米波海量MIMO信道估计器。在估计信道的基础上，采用固有宽带方法设计多用户混合收发器。这些收发器将进一步优化以利用空间复用增益，尽管混合收发器的射频（RF）链数量非常少。除此之外，还将努力解决超低复杂性混合收发器与超低位深度模数转换器和/或超低位计数模拟移相器网络相关的极端系统设计和优化挑战。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

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients

DOI：
10.1609/aaai.v37i9.26337
发表时间：
2023-03
期刊：
ArXiv
影响因子：
0
作者：
Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis
通讯作者：
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

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
影响因子：
0
作者：
Gao, Shijian;Yan, Jia;Giannakis, Georgios B.
通讯作者：
Giannakis, Georgios B.

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