1 citations · 1 across the 10 of their papers we have counts for
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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…