Reinforcement learning for Admission Control in 5G Wireless Networks
Author:
Affiliation:
1. Eindhoven University of Technology,Department of Mathematics and Computer Science,The Netherlands
2. Nokia Bell Labs,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685108.pdf?arnumber=9685108
Reference22 articles.
1. Long Short-Term Memory
2. Call Admission Control Using Neural Network in Wireless Multimedia Networks
3. Call admission control for reducing dropped calls in code division multiple access (CDMA) cellular systems
4. A Scalable and Flexible Radio Access Network Architecture for Fifth Generation Mobile Networks
5. Satisfying Network Slicing Constraints via 5G MAC Scheduling
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1. Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach;International Journal of Communication Systems;2024-07-30
2. Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05
3. Admission Control with Latency Considerations for 5G Mobile Edge Computing;2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM);2023-06
4. Adaptive Generalized Proportional Fair Scheduling with Deep Reinforcement Learning;2022 IEEE Globecom Workshops (GC Wkshps);2022-12-04
5. Reinforcement learning for Admission Control in 5G Wireless Networks;2021 IEEE Global Communications Conference (GLOBECOM);2021-12
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