Qiang Liu |

Qiang Liu

Assistant Professor at University of Nebraska–Lincoln

Dr. Qiang Liu received Ph.D. in Electrical Engineering under Dr. Han’s supervision from the University of North Carolina at Charlotte in Dec. 2021. His research topics are network slicing, wireless edge computing, deep reinforcement learning, and augmented reality.

Jobs

Member of Technical Staff at Nokia Bell Labs, Jan. 2021 - Aug. 2021
Tenure-Track Assistant Professor at University of Nebraska–Lincoln, Aug. 2021 - Present

Latest

RoNet: Toward Robust Neural Assisted Mobile Network Configuration
Atlas: Automate Online Service Configuration in Network Slicing
Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions
Towards Secure and Intelligent Network Slicing for 5G Networks
A survey on sleep mode techniques for ultra-dense networks in 5G and beyond
OnSlicing: Online end-to-end network slicing with reinforcement learning
Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks
LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
When Network Slicing meets Deep Reinforcement Learning
Energy-Efficient Virtual Radio Access Networks for Multi-Operators Cooperative Cellular Networks
DIRECT: Distributed Cross-Domain Resource Orchestration in Cellular Edge Computing
VirtualEdge: Multi-Domain Resource Orchestration and Virtualization in Cellular Edge Computing
Energy-Efficient On-demand Cloud Radio Access Networks Virtualization
Joint Radio and Computation Resource Management for Low Latency Mobile Edge Computing
DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing
On Designing Energy-Efficient Heterogeneous Cloud Radio Access Networks
An Edge Network Orchestrator for Mobile Augmented Reality
Data-Driven Network Optimization in Ultra-Dense Radio Access Networks
Energy-Efficient RRH Sleep Mode for Virtual Radio Access Networks
Demo: Fast and Accurate Object Analysis at the Edge for Mobile Augmented Reality
Demo Abstract: MExR: Mobile Edge Resource Management for Mixed Reality Applications
Computing Resource Aware Energy Saving Scheme for Cloud Radio Access Networks