Collaborative Research: SpecEES: Towards Energy and Spectrally Efficient Millimeter Wave MIMO Platforms - A Unified System, Circuits, and Machine Learning Framework

无线网络自诞生以来就发挥了变革性的社会作用。开发下一代无线网络是美国和世界其他国家的国家优先事项。截至2019年，5G无线网络正处于部署的早期阶段，其目标是能够支持快速增长的移动的数据流量，并在数十亿设备上实现低延迟，同时降低整体网络能耗和成本。以最先进的形式部署5G网络预计将持续数年，直到2020年代。然而，推动无线网络容量的消费者需求预计将继续有增无减，特别是随着自主交通和交付网络等新应用场景的成熟。为了满足这些需求，该项目试图通过采用涵盖电路、系统和人工智能的统一方法来调查“超越5G”网络的硬件和物理层需求。拟议的理论，算法和硬件实现预计将在许多领域产生影响，包括技术转移到行业，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推广，以及所有模拟框架和机器学习数据集的公开发布。 本计画的研究目标是开发一套针对毫米波MIMO系统的分析与设计工具，包括特定的电路感知信号处理技术，以及新颖的算法感知电路设计。基础算法的贡献将用于解决关键的毫米波MIMO系统挑战，例如提高高移动应用和密集毫米波部署中的频谱效率和能源效率。基本电路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RF预编码器和组合器，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目标：（1）研究混合体系结构的联合系统/电路分析和设计方法;（2）开发新颖的电路（包括高效率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）采用机器学习工具来设计电路和部署感知的波束成形码本，并利用机器学习技术设计毫米波干扰感知波束形成;（5）集成到具有适当传感器和执行器的MIMO平台中，以实现上述机器学习技术的硬件实现该奖项反映了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。

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.