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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationINFOCOM 2021 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9780738112817
DOIs
StatePublished - 10 May 2021
Event40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
Duration: 10 May 202113 May 2021

Publication series

NameProceedings - IEEE INFOCOM
Volume2021-May
ISSN (Print)0743-166X

Conference

Conference40th IEEE Conference on Computer Communications, INFOCOM 2021
Country/TerritoryCanada
CityVancouver
Period10/05/2113/05/21

Keywords

  • IEEE 802.11ac
  • Rate adaptation
  • Reinforcement learning
  • Wi-Fi

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