Deep Reinforcement Learning based Rate Adaptation for Wi-Fi Networks
Author:
Affiliation:
1. Sun Yat-sen University,School of Electronics and Communication Engineering,Guangzhou,China
2. Huawei Technologies Co., Ltd,Wireless Technology Lab, 2012 Laboratories,China
Funder
National Key Research and Development Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10012685/10012692/10012797.pdf?arnumber=10012797
Reference13 articles.
1. Reinforcement learning: An introduction;sutton,2018
2. Human-level control through deep reinforcement learning;mnih;Nature,2015
3. Link adaptation based on adaptive modulation and coding for multiple-antenna OFDM system
4. Multi-Agent Reinforcement Learning-Based Distributed Channel Access for Next Generation Wireless Networks
5. Practical Machine Learning-Based Rate Adaptation Solution for Wi-Fi NICs: IEEE 802.11ac as a Case Study
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1. A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21
2. Deep Reinforcement Learning based Dynamic Channel Bonding for Wi-Fi Networks;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21
3. Rate Adaptation Algorithms in IEEE 802.11 Wireless Networks: A Comparative Study;Journal of The Institution of Engineers (India): Series B;2023-10-24
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