An experience driven design for IEEE 802.11ac rate adaptation based on reinforcement learning

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

研究成果: Conference contribution同行評審

13 引文 斯高帕斯(Scopus)

摘要

The IEEE 802.11ac supports gigabit speeds by extending 802.11n air-interface features and increases the number of rate options by more than two times. Enabling so many rate options can be a challenge to rate adaptation (RA) solutions. Particularly, they need to adapt rates to various fast-changing channels; they would suffer without scalability. In this work, we identify three limitations of current 802.11ac RAs on commodity network interface cards (NICs): no joint rate and bandwidth adaptation, lack of scalability, and no online learning capability. To address the limitations, we apply deep reinforcement learning (DRL) into designing a scalable, intelligent RA, designated as experience driven rate adaptation (EDRA). DRL enables the online learning capability of EDRA, which not only automatically identifies useful correlations between important factors and performance for the rate search, but also derives low-overhead avenues to approach highest-goodput (HG) rates by learning from experience. It can make EDRA scalable to timely locate HG rates among many rate options over time. We implement and evaluate EDRA using the Intel Wi-Fi driver and Google TensorFlow on Intel 802.11ac NICs. The evaluation result shows that EDRA can outperform the Intel and Linux default RAs by up to 821.4% and 242.8%, respectively, in various cases.

原文English
主出版物標題INFOCOM 2021 - IEEE Conference on Computer Communications
發行者Institute of Electrical and Electronics Engineers Inc.
頁數10
ISBN(電子)9780738112817
DOIs
出版狀態Published - 10 5月 2021
事件40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
持續時間: 10 5月 202113 5月 2021

出版系列

名字Proceedings - IEEE INFOCOM
2021-May
ISSN(列印)0743-166X

Conference

Conference40th IEEE Conference on Computer Communications, INFOCOM 2021
國家/地區Canada
城市Vancouver
期間10/05/2113/05/21

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