1 citations · 1 across the 10 of their papers we have counts for
12 papers
RadTwin: Generalizable Wireless Digital Twin for Dynamic Environments
Yuru Zhang, Ming Zhao, Qiang Liu +3
Precisely modeling radio propagation in dynamic wireless environments is fundamental to the realization of wireless digital twins. Traditional ray tracing methods rely on accurate…
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.…
Automatic Network Planning with Digital Radio Twin
Xiaomeng Li, Yuru Zhang, Qiang Liu +5
Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to…
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…
AdaMap: High-Scalable Real-Time Cooperative Perception at the Edge
Qiang Liu, Yongjie Xue, Yuru Zhang +2
Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achiev…
RoNet: Toward Robust Neural Assisted Mobile Network Configuration
Yuru Zhang, Yongjie Xue, Qiang Liu +2
Automating configuration is the key path to achieving zero-touch network management in ever-complicating mobile networks. Deep learning techniques show great potential to automatic…
CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing
Yongjie Xue, Yuru Zhang, Qiang Liu +2
Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the ma…
EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
Qiang Liu, Yuru Zhang, Haoxin Wang
High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles. To maintain an up-to-date map, we…