11 citations · 17 across the 5 of their papers we have counts for
5 papers
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Tianlun Hu, Qi Liao, Qiang Liu +2
Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dim…
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning
Tianlun Hu, Qi Liao, Qiang Liu +1
Network slicing enables operators to efficiently support diverse applications on a common physical infrastructure. The ever-increasing densification of network deployment leads to…
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning
Tianlun Hu, Qi Liao, Qiang Liu +1
Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real ne…
HARU: Haptic Augmented Reality-Assisted User-Centric Industrial Network Planning
Qi Liao, Tianlun Hu, Nikolaj Marchenko +2
To support Industry 4.0 applications with haptics and human-machine interaction, 6G requires a new framework that is fully autonomous, visual, and interactive. In this paper, we pr…
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
Qi Liao, Tianlun Hu, Dan Wellington
The legacy mobility robustness optimization (MRO) in self-organizing networks aims at improving handover performance by optimizing cell-specific handover parameters. However, such…