| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | May 15, 2017 |
| Latest Amendment Date: | April 27, 2020 |
| Award Number: | 1704401 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Phillip Regalia
pregalia@nsf.gov (703)292-2981 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2017 |
| End Date: | April 30, 2023 (Estimated) |
| Total Intended Award Amount: | $399,011.00 |
| Total Awarded Amount to Date: | $399,011.00 |
| Funds Obligated to Date: |
FY 2018 = $198,369.00 FY 2020 = $101,347.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
809 S MARSHFIELD AVE M/C 551 CHICAGO IL US 60612-4305 (312)996-2862 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
809 S Marshfield Chicago IL US 60612-4305 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CCF, Comm & Information Foundations |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
CIF: Medium: Collaborative Research:
Low-Resolution Sampling with Generalized Thresholds
Jian Li, Lee Swindlehurst, and Mojtaba Soltanalian
Abstract
Quantization of signals of interest is a necessary first step in digital signal processing applications. When signals across a wide frequency band are of interest, a fundamental tradeoff between sampling rate, amplitude quantization precision, cost, and power consumption is encountered. The investigators study low resolution sampling techniques with general thresholds, which are affordable, technically feasible, easy to apply, energy-efficient, and consistent with technological trends. The enormous gains in capacity and spectral efficiency, for example, that could be provided by a successful millimeter wave (mm-wave) massive multiple-input multiple output implementation could have a revolutionary effect on the performance of wireless systems nearly everywhere we use them: at home, at work, at school, commuting via public transportation or by plane, shopping, at restaurants, recreational venues, sporting events, and so on. Besides consumer applications, there are many military- and security-related scenarios where our systems could be used.
This project involves advancing fundamental knowledge in developing dynamic energy-efficient and cost-effective sampling techniques and applies engineering principles to address the critical needs of several important and related applications. Specifically, this project involves addressing significant open questions, including deterministic identifiability, performance bounds, and impact of thresholding pattern on spectrum sensing and array processing, radio frequency interference mitigation, and mm-wave communications to gain fundamental insights into the novel paradigm of low resolution sampling with general thresholds, devising novel signal processing algorithms, including effective and efficient sparse signal recovery techniques and parametric maximum likelihood methods for enhanced performance, and evaluating and demonstrating the performance using measured data. This project also involves preparing students for engineering in the 21st century through the incorporation of practical design and problem-solving techniques into both the education curriculum.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
Our modern world increasingly relies on rapid data exchange and processing, propelling the quest for innovative and efficient signal processing techniques. This report provides an overview of the research we undertook, aiming to develop new approaches for dealing with low-resolution (particularly one-bit) signal processing - think of it as trying to paint the Mona Lisa with broader brush strokes, yet capturing her enigmatic smile perfectly.
An Unexpected Breakthrough: While common understanding suggested that the more data we have, the harder it is to process, we stumbled upon a fascinating twist - the Sample Abundance singularity. It's like finding out that adding more pieces to a jigsaw puzzle actually makes it easier to solve. Essentially, we observed that as the volume of data grows, the computational requirements for processing that data can surprisingly drop. This insight is especially valuable as our world deals with an increasing amount of data each year, especially in low-resolution settings.
Turning Theory into Practice: One-bit sampling is akin to viewing the world in black and white rather than full color. Though simplistic, this approach enables fast data collection. Among our various endeavors, a few standout results cast a light on how these theories can transform real-world applications:
- Enhanced Covariance Recovery: Signal processing requires understanding the relationship between different parts of the data. In our work, we reformulated the traditional methods used to understand these relationships, enhancing them to account for the dynamic nature of today’s low-resolution data streams.
- Deep Dive into Recovery: We have merged the world of deep learning and low-resolution signal processing. This union paved the way for a “blind recovery” of signals, where signals can be deciphered without extensive prior knowledge about the data collection method.
- The UNO Sampling: We made it possible to combine the best of two worlds in our work– the speed of one-bit sampling and the precision of unlimited sampling techniques.
- Revolutionizing Radar Systems: Our research showcased potential breakthroughs for future radar technology. By embracing low-resolution data processing, we can devise faster, yet precise, radar systems suitable for various applications.
Our journey ventured into the fundamentals of signal processing, challenging established norms and unlocking new frontiers. The beauty lies in realizing that sometimes, “less is more.” These findings, supported by rigorous research and practical applications, are expected to set the foundation for the next generation of communication and sensing technologies, catering to an ever-connected society.
Last Modified: 08/11/2023
Modified by: Mojtaba Soltanalian
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