Optimization of URLLC and eMBB Multiplexing via Deep Reinforcement Learning
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/8844510/8849928/08850168.pdf?arnumber=8850168
Cited by 18 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. Constrained Risk-Sensitive Deep Reinforcement Learning for eMBB-URLLC Joint Scheduling;IEEE Transactions on Wireless Communications;2024-09
2. Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning;International Journal of Computational Intelligence Systems;2024-06-03
3. Subcarrier power control for URLLC communication system via multi-agent deep reinforcement learning in IoT network;International Journal of Communication Networks and Distributed Systems;2024
4. QoS aware resource allocation for coexistence mechanisms between eMBB and URLLC: Issues, challenges, and future directions in 5G;Computer Communications;2024-01
5. Intelligent Resource Allocation for Coexisting eMBB and URLLC Traffic in 5G Industrial Networks;2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics);2023-12-17
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