AWARDS

CAREER: Vision and Learning Augmented D-Band Networking and Imaging

Project Overview

• Principal Investigator: Prof. Sanjib Sur
• Team:
  • Graduate Students: Hem Regmi; Aakriti Adhikari;
  • Undergraduate Students:
• Support: NSF CAREER Program: NSF #2144505 CAREER: Vision and Learning Augmented D-Band Networking and Imaging

Synopsis: 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.

Software-Hardware Reconfigurable Systems for Mobile Millimeter-Wave Networks

Project Overview

• Principal Investigator: Prof. Sanjib Sur
• Co-Principal Investigators: Prof. Srihari Nelakuditi; Prof. Guoan Wang
• Team:
  • Graduate Students: Moh Sabbir Saadat; Jinqun Ge; Hem Regmi; Aakriti Adhikari;
  • Undergraduate Students: Austin Hetherington; Timothy Dayne Hooks; Lance Kevin; Ian McDowell;
• Support: NSF CNS Core Program: NSF #1910853 CNS Core: Small: Software-Hardware Reconfigurable Systems for Mobile Millimeter-Wave Networks

Synopsis: Millimeter-wave is a core technology for next-generation wireless and cellular networks (5G and beyond). Networks using millimeter-wave technologies are expected to satiate the rapidly growing customer appetite for mobile data and to meet the stringent throughput, latency, and reliability requirements of emerging applications, such as immersive virtual and mixed reality, tactile internet, vehicular communications, and autonomous vehicles safety. However, high directionality, high channel dynamics, and sensitivity to blockages render state-of-the-art millimeter-wave technologies unsuitable for low-latency, high performance, and ultra-reliable applications. This research project focuses on designing software-hardware reconfigurable systems to address the key challenges and improve the performance, availability, and reliability of mobile millimeter-wave networks. This project will impact the broader population positively because it yields near-term benefits in 5G infrastructure and paves the way for long-term millimeter-wave research. Furthermore, this project will engage in outreach activities and involve a diverse set of students, particularly, women and minorities, leveraging the experimental nature of the research on next-generation wireless and cellular networks.

The project addresses the key challenges by executing three thrusts: (1) MilliNet: To overcome high signal attenuation, millimeter-wave radios must focus their power via highly directional, electronically steerable beams. But, aligning the beams and maintaining the link between devices during obstruction and mobility are the fundamental barriers toward reliable connection. MilliNet, a faster beam alignment protocol, draws on ideas from the sparse channel recovery, allowing the radios to quickly discern the best physical millimeter-wave paths even under thousands of beams and picocell choices. (2) ReconMilli: To achieve spectrum flexibility, next-generation radios must be able to operate over a wide range of the spectrum, from micro-wave to millimeter-wave. But the fundamental challenge is that physical space on mobile devices is limited. ReconMilli, a reconfigurable antenna design, joins multiple millimeter-wave antennas physically into a micro-wave antenna, but splits it, when needed, into multiple millimeter-wave antennas; thus, achieving spectrum flexibility and saving physical space. (3) LiMesh: To make the deployment and maintenance of a 5G picocell mesh easy, mobile operators will use multi-Gbps fixed millimeter-wave links. Yet, disruptions in the wireless mesh are common; but, more importantly, such disruptions are catastrophic for ultra-reliable connectivity. LiMesh, an ultra-reliable picocell mesh design, leverages the fixed geometrical arrangement of the directional links to infer disruptions using a space-time failure correlation metric proactively. The research project will design, build, and empirically validate the proposed systems in millimeter-wave wireless test-beds.