Reinforcement Learning for Radio Resource Management in RAN Slicing: A Survey

Author:

Zangooei Mohammad1,Saha Niloy1,Golkarifard Morteza1,Boutaba Raouf1

Affiliation:

1. University of Waterloo,Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach;Physical Communication;2024-10

2. Network Selection Over 5G-Advanced Heterogeneous Networks Based on Federated Learning and Cooperative Game Theory;IEEE Transactions on Vehicular Technology;2024-08

3. Smart radio access selection and slice allocation for differentiated traffic management over 6G heterogeneous networks;Proceedings of the ACM Multimedia Systems Conference 2024 on ZZZ;2024-04-15

4. Flexible RAN Slicing in Open RAN With Constrained Multi-Agent Reinforcement Learning;IEEE Journal on Selected Areas in Communications;2024-02

5. Resource Allocation in Multi-Cell Networks: A Deep Reinforcement Learning Approach;2023 14th International Conference on Information and Communication Technology Convergence (ICTC);2023-10-11

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