Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE, THE
Doing Business As Name:University of North Carolina at Charlotte
PD/PI:
  • Tao Han
  • (704) 687-8406
  • tao.han@uncc.edu
Award Date:05/03/2021
Estimated Total Award Amount: $ 449,779
Funds Obligated to Date: $ 88,188
  • FY 2021=$88,188
Start Date:05/15/2021
End Date:04/30/2026
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: AutoEdge: Deep Reinforcement Learning Methods and Systems for Network Automation at Wireless Edge
Federal Award ID Number:2047655
DUNS ID:066300096
Parent DUNS ID:142363428
Program:Networking Technology and Syst
Program Officer:
  • Alexander Sprintson
  • (703) 292-2170
  • asprints@nsf.gov

Awardee Location

Street:9201 University City Boulevard
City:CHARLOTTE
State:NC
ZIP:28223-0001
County:Charlotte
Country:US
Awardee Cong. District:12

Primary Place of Performance

Organization Name:University of North Carolina at Charlotte
Street:9201 University City Blvd
City:Charlotte
State:NC
ZIP:28223-0001
County:Charlotte
Country:US
Cong. District:12

Abstract at Time of Award

The next-generation wireless technology promises advanced network capabilities with extremely high bandwidth and ultra-low latency that will catalyze a wide range of new mobile services and customer applications in vertical sectors such as transport, media, and manufacturing. The explosion of networking connections and the diversification of network services will dramatically increase the complexity of network management. This CAREER project aims to develop domain-specific deep reinforcement learning (DRL) methods and systems to automate the configuration, provisioning, and orchestration of network resources and services in next-generation wireless edge computing networks. The successful completion of this CAREER project will advance the understanding of the inherent relationships among DRL, communications, computing, and networking and lay a solid foundation for studying learning-based algorithms and systems for network automation in wireless edge computing. Besides, the technologies developed in the project will significantly reduce the operational cost of wireless networks and thus allow affordable high-performance wireless connectivity for all communities including low-income and remote communities. Moreover, the project provides interdisciplinary education to cultivate next-generation engineers and researchers who master both advanced wireless and Artificial Intelligence (AI) technologies via the integration of research into education and industrial-academic and cross-disciplinary collaborations. This CAREER project aims to develop deep reinforcement learning (DRL) methods and systems that automate end-to-end resource orchestration in wireless edge computing networks. Toward this end, two fundamental research problems are investigated: 1) how to design domain-specific DRL that can effectively solve end-to-end orchestration problems in large-scale wireless edge computing networks and 2) how to efficiently deploy DRL-based orchestration solutions in large-scale networking systems. To solve the first problem, the project studies the design of states, reward functions, training algorithms, and neural networks of domain-specific DRL, develops methods of handling various constraints in DRL-based end-to-end resource orchestration to avoid constraint violations, and designs context-aware multi-agent DRL methods to leverage domain knowledge of wireless edge computing to improve the learning efficiency of DRL. To solve the second problem, this project develops policy distillation methods to address the DRL deployment issues caused by the divergence between network simulations and real network systems, and designs cross-scale knowledge transfer methods to address the DRL deployment issues caused by the mismatch of the dimensions of small-scale testbeds and large-scale wireless edge computing systems. The project also develops an augmented network simulator and an edge computing system prototype for evaluating DRL-based end-to-end orchestration solutions. 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.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.