AI-Driven rApps for Reducing Radio Access Network Interference in Real-World 5G Deployment

To efficiently operate 5G radio access networks (RAN) under a variety of environments and use cases that change over time, it is important to intelligently manage the RAN and reprogram its configuration dynamically as needed. The Non-Real-Time RAN Intelligent Controllers (Non-RT RIC) as defined by O...

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Bibliographic Details
Main Authors: Tran, Nguyen-Bao-Long, Ngo, Mao V., Pua, Yong Hao, Le, Thanh-Long, Chen, Binbin, Quek, Tony
Format: Conference Proceeding
Language:English
Subjects:
5G mobile communication
AI-based RAN Optimization
Computational modeling
Conferences
Intelligent networks
Interference
Location awareness
Non-RT RIC
O-RAN
Open RAN
Programmable RAN
SG Networks
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Summary:To efficiently operate 5G radio access networks (RAN) under a variety of environments and use cases that change over time, it is important to intelligently manage the RAN and reprogram its configuration dynamically as needed. The Non-Real-Time RAN Intelligent Controllers (Non-RT RIC) as defined by O-RAN ALLIANCE can serve a key role towards such programmable RAN, by supporting AI-based rApps that can infer the best RAN operating configurations based on gathered information from RAN. In this demo, we present a series of rApps that we have developed based on O-RAN Software Community (O-RAN SC)'s Non-RT RIC. These rApps together provide localization, UE throughput prediction, and cross-cell interference management functionalities. We have successfully integrated our rApps with a commercial-grade 5G system that is deployed in our campus. Our demo shows significant end-to-end performance gain that can be obtained by using AI-driven rApps in a real-world dynamic environment.
ISSN:2833-0587
DOI:10.1109/INFOCOMWKSHPS61880.2024.10620743