42 citations · 47 across the 8 of their papers we have counts for
8 papers
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…
Atlas: Automate Online Service Configuration in Network Slicing
Qiang Liu, Nakjung Choi, Tao Han
Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on ser…
Real-Time Dynamic Map with Crowdsourcing Vehicles in Edge Computing
Qiang Liu, Tao Han, Jiang +2
Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definit…
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…
DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets
Yongjie Guan, Xueyu Hou, Nan Wu +2
Mobile headsets should be capable of understanding 3D physical environments to offer a truly immersive experience for augmented/mixed reality (AR/MR). However, their small form-fac…
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…