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Found 5 papers, 0 papers with code
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Based on the proposed framework, we design a new neural-assisted algorithm to allocate radio resources to slices to maximize the network utility under inter-slice resource constraints.
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning
Network slicing enables operators to efficiently support diverse applications on a common physical infrastructure.
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning
In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning.
HARU: Haptic Augmented Reality-Assisted User-Centric Industrial Network Planning
To support Industry 4. 0 applications with haptics and human-machine interaction, 6G requires a new framework that is fully autonomous, visual, and interactive.
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
The legacy mobility robustness optimization (MRO) in self-organizing networks aims at improving handover performance by optimizing cell-specific handover parameters.
