CIF: Small: Signal Processing and Learning for NOMA Millimeter-Wave Massive MIMO Systems

With the proliferation of versatile devices and data-consuming services, the quest for spectrum efficiency has led to the combination of three disruptive technologies, namely millimeter-wave (mmWave) communications, massive multiple-input multiple-output (maMIMO), and non-orthogonal multiple access (NOMA). To fully understand the niche role the combined NOMA-mmWave-maMIMO technology could be playing in future wireless systems, this project develops sparse signal-processing and advance machine-learning techniques for efficient sensing and system optimization. The outcomes of this project are expected to make NOMA-mmWave-maMIMO an attractive candidate to support low-power high-data-rate applications in dense urban environments, including wireless broadband access, machine-type communications and Internet of Things. This project provides ample opportunities to students for research and workforce development, and further broadens societal impacts via curriculum development, research dissemination and public outreach. The spectrum sharing paradigm of NOMA needs to be re-engineered to account for the unique angular selectivity of mmWave and maMIMO channels. To this end, this project addresses research challenges related to spectral efficiency, energy efficiency, and intelligence, with focuses on (i) accurate and efficient channel sensing under hardware constraints and in practical settings, and on (ii) the holistic integration of signal processing and machine learning for system enhancement and real-time design optimization in NOMA-mmWave-maMIMO systems. Proposed ideas and methods combine multi-disciplinary research concepts and methodologies from wireless communications, signal processing, optimization and machine learning. This research effort is expected to advance the design of 5G wireless systems and beyond, but also to have a beneficial impact on the development of future intelligent wireless applications. 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.