Collaborative Research: SpecEES: Towards Energy and Spectrally Efficient Millimeter Wave MIMO Platforms - A Unified System, Circuits, and Machine Learning Framework
合作研究:SpecEES:迈向能源和频谱高效的毫米波 MIMO 平台 - 统一的系统、电路和机器学习框架
基本信息
- 批准号:1923858
- 负责人:
- 金额:$ 45万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Wireless networks have played a transformative societal role since their inception. The development of the next generation of wireless networks is a national priority for the United States and other countries around the world. As of 2019, 5G wireless networks are in early stages of deployment, and have the goals of being able to support rapidly increasing mobile data traffic with low latency across billions of devices, while reducing overall network energy consumption and cost. The deployment of 5G networks in their most advanced form is expected to take several years lasting well into the 2020's. However, consumer demands that drive wireless network capacity is projected to continue unabated, especially as new application scenarios such as autonomous transportation and delivery networks mature. In anticipation of such needs, this project seeks to investigate hardware and physical layer needs of "beyond 5G" networks by taking a unified approach that encompasses circuits, systems and artificial intelligence. The proposed theories, algorithms, and hardware implementation are expected to have impacts in a number of areas that include technology transfer to industry, development of undergraduate and graduate course materials, graduate student training, undergraduate research experiences and community outreach via wireless testbed development, and public release of all simulation frameworks and machine learning datasets. The research goal of this project is to develop a set of analysis and design tools for mm-wave MIMO systems including specific circuits-aware signal processing techniques, and novel algorithms-aware circuit designs. Fundamental algorithmic contributions will be made to solve key mm-wave MIMO system challenges such as enhancing the spectral efficiency and energy efficiency in highly-mobile applications and dense mm-wave deployments. Fundamental circuit contributions will include solutions to designing energy-efficient MIMO transmitters, designing energy- and area-efficient RF precoders and combiners, and designing platforms to support machine learning algorithms. The project has several inter-related thrusts: (1) Investigate joint system/circuit analysis and design approaches for hybrid architectures; (2) Develop novel circuits (including high-efficiency transmitters and bi-directional signal paths) to enable high energy-efficiency, reconfigurability and concurrent multi-band operation in hybrid MIMO architectures (3) Adopt machine learning tools to design circuits- and deployment-aware beamforming codebooks, and leverage machine learning techniques to design mm-wave interference-aware beamforming; (5) Integrate into MIMO platforms with appropriate sensors and actuators to enable hardware implementation of the aforementioned machine learning techniquesThis 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.
无线网络从一开始就扮演着变革性的社会角色。下一代无线网络的发展是美国和世界其他国家的国家优先事项。截至2019年,5G无线网络处于早期部署阶段,其目标是能够在数十亿台设备上以低延迟支持快速增长的移动数据流量,同时降低整体网络能耗和成本。5G网络最先进形式的部署预计需要数年时间,一直持续到2020年。然而,消费者对推动无线网络容量的需求预计将持续不减,特别是随着自动运输和交付网络等新应用场景的成熟。考虑到这些需求,该项目旨在通过采用涵盖电路、系统和人工智能的统一方法,研究“超越5G”网络的硬件和物理层需求。所提出的理论、算法和硬件实现预计将在许多领域产生影响,包括技术转移到工业、本科和研究生课程材料的开发、研究生培训、本科研究经验和通过无线测试平台开发的社区外展,以及所有模拟框架和机器学习数据集的公开发布。本项目的研究目标是为毫米波MIMO系统开发一套分析和设计工具,包括特定的电路感知信号处理技术和新颖的算法感知电路设计。基础算法将为解决关键的毫米波MIMO系统挑战做出贡献,例如在高移动应用和密集毫米波部署中提高频谱效率和能源效率。基础电路的贡献将包括设计节能MIMO发射机的解决方案,设计节能和面积高效的射频预编码器和合并器,以及设计支持机器学习算法的平台。该项目有几个相互关联的重点:(1)研究混合架构的联合系统/电路分析和设计方法;(2)开发新型电路(包括高效发射机和双向信号路径),以实现混合MIMO架构中的高能效、可重构性和并发多频带操作。(3)采用机器学习工具设计电路和部署感知波束形成码本,并利用机器学习技术设计毫米波干扰感知波束形成;(5)将适当的传感器和执行器集成到MIMO平台中,使上述机器学习技术的硬件实现成为可能。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。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Jeyanandh Paramesh其他文献
Multi-rate Polyphase DSP and LMS Calibration Schemes for Oversampled ADCs
- DOI:
10.1007/s11265-012-0677-3 - 发表时间:
2012-06-07 - 期刊:
- 影响因子:1.800
- 作者:
Subhanshu Gupta;Yi Tang;Jeyanandh Paramesh;David J. Allstot - 通讯作者:
David J. Allstot
Jeyanandh Paramesh的其他文献
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{{ truncateString('Jeyanandh Paramesh', 18)}}的其他基金
SBIR Phase I: Simultaneous Transmit-Receive and Full-Duplex Millimeter-Wave Massive Multiple-Input and Multiple-Output (MIMO) Systems
SBIR 第一阶段:同时发送-接收和全双工毫米波大规模多输入多输出 (MIMO) 系统
- 批准号:
2322297 - 财政年份:2023
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
Collaborative Research: SpecEES: Towards Energy and Spectrally Efficient Millimeter Wave MIMO Platforms - A Unified System, Circuits, and Machine Learning Framework
合作研究:SpecEES:迈向能源和频谱高效的毫米波 MIMO 平台 - 统一的系统、电路和机器学习框架
- 批准号:
2001135 - 财政年份:2019
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
Reconfigurable All-Digital CMOS Frequency Synthesizers for Cognitive and Milimeter-Wave Radios
用于认知和毫米波无线电的可重构全数字 CMOS 频率合成器
- 批准号:
1309927 - 财政年份:2013
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
EARS: Title: Energy-Efficient Millimeter-wave Communication via Adaptation and Reconfiguration
EARS:标题:通过适应和重新配置实现节能毫米波通信
- 批准号:
1343324 - 财政年份:2013
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
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