42 citations · 47 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions
Qiang Liu, Nakjung Choi, Tao Han
5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these use cases cost-effectively, network sl…
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning
Qiang Liu, Nakjung Choi, Tao Han
Network slicing allows mobile network operators to virtualize infrastructures and provide customized slices for supporting various use cases with heterogeneous requirements. Online…
Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks
Qiang Liu, Nakjung Choi, Tao Han
Network slicing is a promising technology that allows mobile network operators to efficiently serve various emerging use cases in 5G. It is challenging to optimize the utilization…