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: 1923858
• 负责人: Jeyanandh Paramesh
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923858&HistoricalAwards=false
• 关键词: Collaborative; Research; SpecEES; Towards; Energy

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.

自成立以来，无线网络已经发挥了变革性的社会作用。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无线网络处于部署的早期阶段，并且目标是能够支持在数十亿个设备中迅速增加移动数据流量，同时降低了整体网络能源消耗和成本。预计以最先进的形式部署5G网络将需要几年的时间才能持续到2020年代。但是，消费者要求预计驱动无线网络容量将继续不断减弱，尤其是随着新的应用程序方案（例如自动运输和交付网络）的成熟。为了期待这种需求，该项目试图通过采用统一的方法，包括电路，系统和人工智能来研究“超越5G”网络的硬件和物理层需求。拟议的理论，算法和硬件实施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发展，研究生培训，本科研究经验以及通过无线测试床的发展以及社区外展览以及社区外展览所有模拟框架和机器学习数据集的公开发布。 该项目的研究目的是为MM-WAVE MIMO系统开发一套分析和设计工具，包括特定电路感知信号处理技术以及新颖的算法感知电路电路设计。将做出基本算法贡献，以解决关键的MM-Wave MIMO系统挑战，例如提高高度移动性应用程序的光谱效率和能源效率以及密集的MM波部署。基本电路的贡献将包括设计能节能的MIMO发射器，设计能源和区域效率的RF预编码器和组合器以及设计平台以支持机器学习算法的解决方案。该项目有几个相互关联的推力：（1）研究混合体系结构的关节系统/电路分析和设计方法； （2）开发新型电路（包括高效发射器和双向信号路径），以实现高能效率，可重新配置性和在混合MIMO架构中同时进行多波段操作（3）采用机器学习工具来设计电路和部署 - - 了解波束形成的代码簿，并利用机器学习技术来设计MM波干扰 - 感知的波束形成； （5）与适当的传感器和执行器集成到MIMO平台中，以实现上述机器学习技术奖的硬件实施，反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来支持的，并具有更广泛的影响。