An Approach to Transmitting URLLC Data with Different Latency Requirements over eMBB Services Based on Deep Reinforcement Learning
Author:
Funder
National Natural Science Foundation of China
Research and Development
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9674217/9674209/09674281.pdf?arnumber=9674281
Reference14 articles.
1. A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR
2. Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networks
3. Radio Resource Allocation for Achieving Ultra-Low Latency in Fog Radio Access Networks
4. 5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
5. Proximal policy optimization algorithms;schulman,2017
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1. Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL;IEEE Transactions on Green Communications and Networking;2024-09
2. eMBB-URLLC Multiplexing: A Greedy Scheduling Strategy for URLLC Traffic with Multiple Delay Requirements;TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON);2023-10-31
3. Puncture-Predictive Fairness Scheduling Scheme for eMBB and URLLC Based on TD3 Algorithm;2023 IEEE/CIC International Conference on Communications in China (ICCC);2023-08-10
4. Hybrid Puncturing and Superposition Scheme for Multiplexing uRLLC and eMBB Services Based on Deep Reinforcement Learning;2022 IEEE 8th International Conference on Computer and Communications (ICCC);2022-12-09
5. Joint User Association and Resource Allocation in Multi-IRSs URLLC Systems;2022 IEEE/CIC International Conference on Communications in China (ICCC);2022-08-11
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