Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning

Author:

Hu Tianlun1,Liao Qi1,Liu Qiang2,Carle Georg3

Affiliation:

1. Nokia Bell Labs,Stuttgart,Germany

2. University of Nebraska Lincoln,United States

3. Technical University of Munich,Germany

Publisher

IEEE

Reference19 articles.

1. Transfer reinforcement learning aided distributed network slicing resource optimization in industrial IoT;mai;IEEE Trans-actions on Industrial Informatics,2021

2. Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

3. Connections Between Spectral Properties of Asymptotic Mappings and Solutions to Wireless Network Problems

4. Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning

5. Transfer learning in deep reinforcement learning: A survey;zhu;CoRR,2020

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1. A transfer learning model for cognitive electronic reconnaissance of unmanned aerial vehicle: Experiments;Engineering Applications of Artificial Intelligence;2024-11

2. Multi-Domain Scaling Algorithm with Inter-Orchestrator Communication for Beyond 5G/6G Networks;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

3. Leveraging Transfer Learning for Production-Aware Slicing in Industrial Networks;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06

4. Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey;IEEE Access;2023

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