TY - GEN
T1 - An experience driven design for IEEE 802.11ac rate adaptation based on reinforcement learning
AU - Chen, Syuan Cheng
AU - Li, Chi-Yu
AU - Chiu, Chui Hao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - 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.
AB - 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.
KW - IEEE 802.11ac
KW - Rate adaptation
KW - Reinforcement learning
KW - Wi-Fi
UR - http://www.scopus.com/inward/record.url?scp=85111912811&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM42981.2021.9488876
DO - 10.1109/INFOCOM42981.2021.9488876
M3 - Conference contribution
AN - SCOPUS:85111912811
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2021 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE Conference on Computer Communications, INFOCOM 2021
Y2 - 10 May 2021 through 13 May 2021
ER -