This project is supported by NSF #2428427 (01/01/2025-12/31/2026).
The lack of network infrastructure in the Midwest and EPSCoR states has widened the digital and economic divide between rural and urban America. Open radio access network (O-RAN) initiatives have gained significant momentum in revolutionizing, defining, and shaping next-generation mobile networks, including Beyond 5G and 6G. In O-RAN mobile networks, network management plays a critical role in overseeing various aspects of network infrastructure, including service orchestration in non-RT RICs and resource allocation in near-RT RICs. Existing approaches generally rely on offline-train-online-deploy strategies using homogeneous AI/ML agents, which face challenges such as simulation-to-reality discrepancies and non-stationary learning environments in real-world, large-scale networks. The long-term vision of this project is to achieve autonomous mobile networks for 6G by designing novel AI/ML techniques to address real-world network management challenges, including, but not limited to, safety, scalability, robustness, and practicality. The project's outcomes are expected to significantly reduce the operating expenses (OpEx) of current mobile networks, thereby facilitating the widespread deployment and cost-effective operation of mobile networks across Nebraska, the Midwest, and EPSCoR states, ultimately contributing to bridging the digital divide between rural and urban America.
- Principal Investigator: Dr. Qiang Liu, Assistant Professor, School of Computing, University of Nebraska-Lincoln
- Host Institution: Dr. Hongwei Zhang, Professor, Department of Electrical and Computer Engineering and Department of Computer Science, Iowa State University
- Graduate Student: Xiaomeng Li, School of Computing, University of Nebraska-Lincoln
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