42 citations · 52 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
Qiang Liu, Tao Han, Ephraim Moges
5G and edge computing will serve various emerging use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a p…
Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
Qiang Liu, Tao Han, Nirwan Ansari
There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Owing to the diverse features of physical sy…