42 citations · 48 across the 8 of their papers we have counts for
9 papers
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…
Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning
Tianlun Hu, Qi Liao, Qiang Liu +2
Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, d…
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…
LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
Qiang Liu, Tao Han, Jiang +2
Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme…
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Qiang Liu, Tao Han, Ning Zhang +1
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse…