Deep Learning Based MAC via Joint Channel Access and Rate Adaptation
Author:
Affiliation:
1. Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China
2. Wireless Technology Lab, 2012 Labs,Huawei,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9860273/9860350/09860617.pdf?arnumber=9860617
Reference21 articles.
1. Multi-Agent Reinforcement Learning-Based Distributed Channel Access for Next Generation Wireless Networks
2. Non-uniform time-step deep Q-network for carrier-sense multiple access in heterogeneous wireless networks;yu;IEEE Transactions on Mobile Computing,2020
3. Practical Machine Learning-Based Rate Adaptation Solution for Wi-Fi NICs: IEEE 802.11ac as a Case Study
4. Reinforcement Learning for Rate Adaptation in CSMA/CA Wireless Networks
Cited by 2 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. DRL-based Dynamic Channel Access and SCLAR Maximization for Networks under Jamming;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21
2. Multiple Access in the Era of Distributed Computing and Edge Intelligence;Proceedings of the IEEE;2024
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