6 papers · 1 filter
inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks
Ming Zhao, Yuru Zhang, Qiang Liu +2
Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on bla…
oneTwin: Online Digital Network Twin via Neural Radio Radiance Field
Yuru Zhang, Ming Zhao, Qiang Liu +1
Digital network twin is a promising technology that replicates real-world networks in real-time and assists with the design, operation, and management of next-generation networks.…
InSlicing: Interpretable Learning-Assisted Network Slice Configuration in Open Radio Access Networks
Ming Zhao, Yuru Zhang, Qiang Liu +2
Network slicing is a key technology enabling the flexibility and efficiency of 5G networks, offering customized services for diverse applications. However, existing methods face ch…
AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks
Ming Zhao, Yuru Zhang, Qiang Liu +2
Open radio access networks (e.g., O-RAN) facilitate fine-grained control (e.g., near-RT RIC) in next-generation networks, necessitating advanced AI/ML techniques in handling online…
Learn to Augment Network Simulators Towards Digital Network Twins
Yuru Zhang, Ming Zhao, Qiang Liu
Digital network twin (DNT) is a promising paradigm to replicate real-world cellular networks toward continual assessment, proactive management, and what-if analysis. Existing discu…
LeFi: Learn to Incentivize Federated Learning in Automotive Edge Computing
Ming Zhao, Yuru Zhang, Qiang Liu +1
Federated learning (FL) is the promising privacy-preserve approach to continually update the central machine learning (ML) model (e.g., object detectors in edge servers) by aggrega…