Meta-Bandit: Spatial Reuse Adaptation via Meta-Learning in Distributed Wi-Fi 802.11ax

被引：0

作者：

Iturria-Rivera, Pedro Enrique ^{[1
]}

Chenier, Marcel ^{[2
]}

Herscovici, Bernard ^{[2
]}

Kantarci, Burak ^{[1
]}

Erol-Kantarci, Melike ^{[1
]}

机构：

[1] University of Ottawa, School of Electrical Engineering and Computer Science, Ottawa, K1N 6N5, ON

[2] Netexperience Inc., Ottawa, K2B 8K2, ON

来源：

IEEE Networking Letters | 2023年 / 5卷 / 04期

关键词：

deep transfer reinforcement learning; Meta-learning; meta-reinforcement learning; multi-agent multi-armed bandits; spatial reuse; Wi-Fi;

D O I：

10.1109/LNET.2023.3268648

中图分类号：

学科分类号：

摘要：

IEEE 802.11ax introduces several amendments to previous standards with a special interest in spatial reuse (SR) to respond to dense user scenarios with high demanding services. In dynamic scenarios with more than one Access Point, the adjustment of joint Transmission Power (TP) and Clear Channel Assessment (CCA) threshold remains a challenge. With the aim of mitigating Quality of Service (QoS) degradation, we introduce a solution that builds on meta-learning and multi-arm bandits. Simulation results show that the proposed solution can adapt with an average of 1250 fewer environment steps and 72% average improvement in terms of fairness and starvation than a transfer learning baseline. © 2019 IEEE.

引用

页码：179 / 183

页数：4