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Found 7 papers, 1 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.
A Generative Approach for Production-Aware Industrial Network Traffic Modeling
We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent stochastic process.
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.
Connections between spectral properties of asymptotic mappings and solutions to wireless network problems
In this study we establish connections between asymptotic functions and properties of solutions to important problems in wireless networks.
