An Experience Driven Design for IEEE 802.11ac Rate Adaptation based on Reinforcement Learning-Reference-Cited by-同舟云学术

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An Experience Driven Design for IEEE 802.11ac Rate Adaptation based on Reinforcement Learning

Published:2021-05-10 Issue: Volume: Page:
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Container-title:IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
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Author:

Chen Syuan-Cheng,Li Chi-Yu,Chiu Chui-Hao

Funder

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9488422/9488423/09488876.pdf?arnumber=9488876

Cited by 14 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. Wireless Multicast Rate Control Adaptive to Application Goodput and Loss Requirements;2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI);2024-05-13

2. ML Framework for Wireless MAC Protocol Design;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05

3. Reinforced-lib: Rapid prototyping of reinforcement learning solutions;SoftwareX;2024-05

4. A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

5. On the Analysis of Computational Delays in Reinforcement Learning-Based Rate Adaptation Algorithms;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

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