Reinforcement Learning for NOMA-ALOHA Under Fading
Author:
Affiliation:
1. Department of Electronic Engineering, University of York, York, U.K.
2. School of Information Technology, Deakin University, Geelong, VIC, Australia
Funder
Australian Government through the Australian Research Council’s Discovery Projects funding scheme
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/26/9920689/09854860.pdf?arnumber=9854860
Cited by 5 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. NOMA-Based ALOHA Protocol for Air-to-Ground Communications With Maximum Transmit Power Limits;IEEE Internet of Things Journal;2024-08-15
2. SSH-MAC: Service-Aware and Scheduling-Based Media Access Control Protocol in Underwater Acoustic Sensor Network;Remote Sensing;2024-07-24
3. Power-Profile in Q-Learning NOMA Random Access Protocols for Throughput Maximization;Journal of Network and Systems Management;2024-05-17
4. A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks;Journal of Sensor and Actuator Networks;2024-04-30
5. Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06
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