| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | August 20, 2020 |
| Latest Amendment Date: | August 18, 2022 |
| Award Number: | 2030026 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Huaiyu Dai
hdai@nsf.gov (703)292-4568 ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | September 15, 2020 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $220,000.00 |
| Total Awarded Amount to Date: | $270,000.00 |
| Funds Obligated to Date: |
FY 2022 = $50,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
201 OLD MAIN UNIVERSITY PARK PA US 16802-1503 (814)865-1372 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
13A EE West University Park PA US 16802-1503 |
| 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): |
SWIFT-Spectrum Innov Futr Tech, SII-Spectrum Innovation Initia |
| Primary Program Source: |
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): | 47.041, 47.049 |
ABSTRACT
This project develops a novel online learning based framework for distributed low-cost devices to efficiently and effectively access the shared spectrum without spectrum sensing. It specifically focuses on no-sensing devices that do not have the powerful radio-frequency (RF) components to enable wideband spectrum sensing, and addresses the cross-technology spectrum access problem in a decentralized setting. A pertinent application the proposed solution addresses is the dynamic spectrum access of Internet-of-Things (IoT) devices that are deployed in either unlicensed or lightly licensed spectrum, in which the distributed IoT devices need to coexist with other active systems. The no-sensing spectrum access and sharing framework has the potential to revolutionize the operation and management of modern and future wireless networks, considerably enhance the spectrum utilization efficiency, and dramatically alleviate the constantly increasing pressure on the limited radio spectrum. The cross disciplinary nature of the research would naturally translate into case studies and projects in a number of undergraduate and graduate level courses taught by the PIs in areas of communications, machine learning, and networking.
This project aims to develop a suite of online learning based spectrum access algorithms for no-sensing devices to coexist with other active systems. The first study focuses on improving the learning efficiency by introducing the best arm identification framework and proposing meta-learning and good channel identification algorithms. The second thrust is devoted to designing spectrum access mechanisms that can seamlessly integrate hybrid automatic repeat request (HARQ). Novel algorithms will be designed to learn the optimal sequence of channels for possible retransmissions, and enhanced for fine-grained control that captures the coding level behavior of HARQ. The last thread of investigation considers multi-user multi-technology coexistence and will develop implicit-communication based distributed spectrum access algorithms. Finally, a thorough validation of the algorithms and spectrum access schemes will be performed using a lab testbed and real-world datasets.
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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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 project aims to develop a novel online learning-based framework for distributed low-cost devices to efficiently and effectively access the shared spectrum without spectrum sensing. The major objective is to develop a suite of novel online learning algorithms, in particular multi-armed bandits (MAB) and reinforcement learning (RL), that can be used to improve spectrum access for no-sensing devices to coexist with other active systems.
Throughout the duration of this project, the team mainly focuses on two research directions: On one side, it focuses on the theoretical side of MAB and RL, and strives to unveil the theoretical underpinnings of modern online learning algorithms. On the other side, with the insights obtained from the theoretical study, it develops practical algorithms that efficiently solve various spectrum sharing and wireless network optimization problems. The results obtained from this project significantly advance the understanding of MAB and RL in the research community, and provide efficient solutions for spectrum sharing and co-existence.
The MAB and RL models and algorithms improve the state of the art in machine learning and sequential decision making, and can be applied to a variety of disciplines, such as autonomous driving, smart cities, recommender systems, etc. This research has the potential to transform the modern spectrum access paradigm, enabling more intelligent, adaptive, and context-aware network management solutions.
Three Ph.D. students have been trained through this project. They learned how to use the analytical tools and computer simulations to study both wireless communication networks and machine learning. The project promotes their creativity and independent problem-solving abilities. Results obtained from this project have been published in top venues in machine learning, information theory, signal processing, and communications. The research results have also been incorporated into EE597: Selected Topics on Reinforcement Learning, a graduate level course developed by the PI at PSU.
Last Modified: 12/20/2024
Modified by: Jing Yang
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