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Found 7 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.
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security.
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 Distributed Intelligence Architecture for B5G Network Automation
The management of networks is automated by closed loops.
Decorrelating Adversarial Nets for Clustering Mobile Network Data
Deep learning will play a crucial role in enabling cognitive automation for the mobile networks of the future.
Neural Network-based Quantization for Network Automation
Deep Learning methods have been adopted in mobile networks, especially for network management automation where they provide means for advanced machine cognition.
