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

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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.