On using Deep Reinforcement Learning to reduce Uplink Latency for uRLLC services
Author:
Affiliation:
1. EURECOM,Sophia Antipolis,France
2. CSC-IT Center for Science Ltd.,Espoo,Finland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001395.pdf?arnumber=10001395
Reference17 articles.
1. Radio Resource Management in Multi-numerology 5G New Radio featuring Network Slicing
2. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
3. A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
Cited by 3 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. Optimization of Energy Efficiency for Uplink mURLLC Over Multiple Cells Using Cooperative Multiagent Reinforcement Learning;IEEE Internet of Things Journal;2024-05-01
2. An Efficient DRL-Based Link Adaptation for Cellular Networks with Low Overhead;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21
3. Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial;Proceedings of the IEEE;2023-11
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