Award Abstract # 1823235
CRI: II-New: Mobile Millimeter-Wave MIMO Network Based on CMU Chipscale Beamformers

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: CARNEGIE MELLON UNIVERSITY
Initial Amendment Date: August 9, 2018
Latest Amendment Date: August 13, 2020
Award Number: 1823235
Award Instrument: Standard Grant
Program Manager: Alhussein Abouzeid
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2018
End Date: September 30, 2023 (Estimated)
Total Intended Award Amount: $968,543.00
Total Awarded Amount to Date: $968,543.00
Funds Obligated to Date: FY 2018 = $968,543.00
History of Investigator:
  • Larry Carley (Principal Investigator)
    carley@ece.cmu.edu
  • Jeyanandh Paramesh (Co-Principal Investigator)
  • Swarun Kumar (Co-Principal Investigator)
  • Jeyanandh Paramesh (Former Principal Investigator)
Recipient Sponsored Research Office: Carnegie-Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3890
(412)268-8746
Sponsor Congressional District: 12
Primary Place of Performance: Carnegie-Mellon University
5000 Forbes Avenue
Pittsburgh
PA  US  15213-3890
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): U3NKNFLNQ613
Parent UEI: U3NKNFLNQ613
NSF Program(s): CCRI-CISE Cmnty Rsrch Infrstrc
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7359
Program Element Code(s): 735900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Current "4G" wireless networks will not be able to support the projected mobile internet traffic, which is projected to grow to 66,000 petabytes (PB) per month in 2022, up from 94 PB/month in 2010 - a growth rate of 72% per year. The key to meeting such demand lies in: (1) the exploitation of new millimeter-wave (mmWave) spectrum above 30 Gigahertz (GHz), and (2) the development of new hardware and algorithms that enable mmWave communication. However, mmWave communication networks are in their infancy, with emerging 5G networks expected to support first generation concepts. Researchers are already looking beyond 5G, and accordingly, there is intense ongoing research in developing fundamental technologies and algorithms for future mmWave networks. Unfortunately, experimental validation and experiment-driven discovery has been largely lacking due to the unavailability of hardware technologies, compounded by high costs that can be supported only by large companies. The proposed project seeks to build new institutional infrastructure that can enable advanced wireless networking experimentation in the mmWave frequency bands. The resulting testbed will provide experimentation tools and seed research for the hardware, physical and network communication communities, and for systems researchers who focus on localization, autonomous driving, indoor navigation, assistive devices for the visually impaired etc. This project will offer opportunities for undergraduate and graduate education, both during the development of the infrastructure, and during its actual use. In addition, students will develop skills in chip implementation, printed circuit board design and fabrication, laboratory characterization skills, using software-defined radios, field-programmable gate array programming skills, all of which are extremely valuable for a career in the technology industry.

This project aims to develop a first-of-its-kind mmWave multiple-input-multiple-output (MIMO) capable network testbed comprising base stations and mobile user modules spanning indoor and outdoor spaces. Testbed development will be led by a two-investigator team combining expertise in mmWave chip/system design with expertise in cross-layer design and implementation of wireless networks. Since commercial mmWave hardware (especially with advanced features) will not be widely available for the foreseeable future, the proposed infrastructure will leverage the principal investigator's previous NSF-funded research that has resulted in the design and prototype demonstration of advanced mmWave radio chips which feature unprecedented levels of integration, energy-efficiency, reconfigurability and programmability. The proposed project is structured to operationalize a small-scale link/network in the early part of the second year, and scaling up in complexity and network density in the second and third years.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Akarsh Prabhakara, Diana Zhang "Exploring mmWave Radar and Camera Fusion for High-Resolution and Long-Range Depth Imaging" IROS , 2022 Citation Details
Rong, Chao and Paramesh, Jeyanandh and Carley, L. Richard "A Deep Reinforcement Learning Framework for High-Dimensional Circuit Linearization" IEEE Transactions on Circuits and Systems II: Express Briefs , v.69 , 2022 https://doi.org/10.1109/TCSII.2022.3183156 Citation Details
Rong, Chao and Paramesh, Jeyanandh and Carley, L. Richard "An Efficient Meta-Reinforcement Learning Approach for Circuit Linearity Calibration via Style Injection" MidWest Symposium on Circuits and Systems , 2023 https://doi.org/10.1109/MWSCAS57524.2023.10406135 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This grant resulted in development of infrastructure to support wireless local area network (WLAN) testbeds, operating in 28GHz and 39GHz bands (mmWave), comprised of base stations and mobile user modules spanning indoor and outdoor spaces. One particularly significant development was first-of-its-kind MIMO RF transceivers operating simultaneously in the 28GHz and 39GHz bands. The capabilities and flexibility demonstrated by the chips developed under this grant far surpasses that of available commercial mmWave hardware. The HBF3 and HBF4 chips resulting from this grant were the first integrated circuit demonstrations of (a) concurrent/reconfigurable beamforming in two different frequency bands, and (b) full-duplex multi-stream beamforming. The HBF3 chips were the first published demonstration of an integrated bi-directional full-duplex link. That is, an HBF3 chip was able to send and receive data simultaneously using the same frequency band for transmission and reception. These results were presented at a major IEEE conference, MobiCom, in 2020. Further, the level of integration achieved in the subsequent RF transceiver chips, HBF4, was exceptional. HBF4 integrated 8 parallel transceiver channels each one incorporating all of the RF functions of a wireless network access point except for the local oscillator/frequency synthesizer. HBF4 even included on-chip digital hardware to support the functions of beam steering, null steering and full-duplex interference cancellation. The researchers also developed a design for an 8-element antenna array (a 2 x 4 MiMO antenna array) that could be driven by a single HBF4 chip mounted directly on the antenna array printed circuit board. The results of this groundbreaking RF chip design research work were published in leading circuit design venues such as two papers at the International Solid-State Circuits Conference (ISSCC) and two papers in the Journal of Solid-State Circuits (JSSC). In addition, Dr. Susnata Mondal, the PhD student who led the design of HBF4, was awarded the Carnegie Mellon University, A.G. Milnes award for the most outstanding PhD Thesis in the Electrical and Computer Engineering Department in the 2020/2021 school year.

This grant also resulted in new research on how the high-bandwidth and beam steering capabilities of mmWave transceivers could also be used for sensing in addition to being used for communications. Work on this grant contributed to development of the first on-automobile mmWave sensing system that can accurately measure tire wear continuously and that is robust to road debris. This work’s key innovation was to leverage high-volume, automobile mmWave radar, by placing it in the tire well of automobiles, and by observing reflections of the radar’s signal from the tire surface and grooves to measure tire wear, even in the presence of debris. This work also demonstrated the ability to detect and locate unsafe, metallic foreign objects such as nails lodged in the tire while driving. Work on this grant also demonstrated another sensor application for vehicles which need to accurately sense lane markers, road signs, etc. The researchers developed and tested an ultra-low-power mmWave reflector that could be attached to roadside objects allowing them to be localized at high accuracy over extended distances using mmWave infrastructure. For example, this research allows vehicles to efficiently localize roadside infrastructure such as lane markers and road signs, even if they are obscured from view by fog. The crucial free space path loss problem experienced by signals propagating at mmWave frequencies was addressed by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Detailed experimental testing indoors and outdoors demonstrated a scalable system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW). A third sensor application that was pioneered under this grant created a mmWave-based platform for robotic geo-fencing and surveillance systems that require accurate monitoring of objects if/when they violate perimeter restrictions. For this application, the researchers explored a solution for depth imaging of objects of interest at high accuracy (few tens of cm) over extended ranges (up to 300 meters) from a single vantage point. The researchers developed a novel solution to this problem that demonstrates long-range depth imaging of objects of interest by fusing the strengths of mmWave radar and optical cameras. Unlike cameras, mmWave radars offer excellent cm-scale depth resolution even at very long ranges. However, their angular resolution is at least 10x worse than that of camera systems. Fusing these two modalities is natural, but in scenes with high clutter and at long ranges, radar reflections are weak and experience spurious artifacts. The research explored a detailed evaluation of the depth imaging capabilities in 400 diverse scenarios and demonstrated that, by appropriately fusing the mmWave radar and optical images, estimates of the depth of static objects up to 90 m away and moving objects up to 305 m away could be obtained with decimeter accuracy.




Last Modified: 02/01/2024
Modified by: Larry R Carley

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