On using Deep Reinforcement Learning to dynamically derive 5G New Radio TDD pattern
Author:
Affiliation:
1. CSC-IT Center for Science Ltd.,Espoo,Finland
2. EURECOM,Sophia-Antipolis,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685820.pdf?arnumber=9685820
Reference13 articles.
1. Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet
2. Towards service-oriented soft spectrum slicing for 5G TDD networks
3. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
4. Deep Reinforcement Learning: A Brief Survey
5. Dynamic TDD transmissions in homogeneous small cell networks
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1. Spatial Deep Learning-Based Dynamic TDD Control for UAV-Assisted 6G Hotspot Networks;IEEE Transactions on Industrial Informatics;2024-09
2. ML-Based What-If Analysis to Mitigate Interference for 5G NR Non-Public Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09
3. A Novel QoS Guaranteed Joint Resource Allocation Framework for 5G NR with Supplementary Uplink Transmission;Electronics;2023-03-26
4. Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment;IEEE Transactions on Mobile Computing;2023
5. Retracted on November 1, 2022 : On enabling 5G dynamic TDD by leveraging deep reinforcement learning and O-RAN;Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing;2022-10-03
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