ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Millimeter-wave (mmWave) is the core wireless technology to enable new applications in transportation, entertainment, education, and telemedicine. Specifically, the recent availability of inexpensive hardware above 100 GHz makes the time ripe for bringing D-band (110-170 GHz) mmWave networks to the masses. However, D-band mmWave networks bring new challenges in optimizing the deployment of picocells, coordination and adaptation of mobile links with unprecedentedly wide frequency options, and a disruption-free confluence of networking-imaging. This research project addresses these key challenges and improves the performance, reliability, and usability of mobile D-band networks. The project will design machine learning augmented scalable D-band systems and networks, and integrate them into applications, such as Augmented Reality (AR), drone delivery, and autonomous cars. The research outcomes will impact the broader population by: (1) bringing ubiquitous and high-quality bandwidth to underserved users; (2) enabling efficient use of spectrum to better utilize this nationally important resource; and (3) elevating the utility of networking devices by enabling several critical applications on them. The proposed research will be disseminated through publications, open-source software and datasets, and close collaboration with industry partners. It will be integrated into education by designing new undergraduate and graduate cross-disciplinary wireless curricula and involvement in broader community outreach activities.

This project aims to enable the practical adoption of D-band mmWave networks and applications by solving the fundamental challenges in deployment, link adaptation, coordination, and unified networking-imaging. Specifically, the project explores an optical vision and deep learning augmented paradigm by thoroughly understanding the physical properties of the D-band channel, building measurement-driven empirical and learning models, and designing practical, real-time systems. Successful execution of this project would enable the following. (1) A framework for optimal deployment and a ?what-if? analysis tool to help optimize the cost and benefits of D-band deployment in both indoor and outdoor environments. (2) Link adaptation and coordination protocols that significantly minimize latency and maximize throughput and efficiency for scalable D-band networking. (3) A unified networking-imaging protocol that reduces disruptions to the throughput and latency and overcomes challenges with the channel specularity to enable high-resolution D-band images. The project will design, build, and empirically validate the proposed systems in a D-band testbed, and the testbed will be extended into an educational platform that enhances the knowledge of wireless networking and sensing for students at different levels.

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.

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