Deep Reinforcement Learning based Rate Adaptation for 802.11ac: A Practical Online Approach

• Main Authors: Chen, Syuan-Cheng, 陳軒丞
• Other Authors: Li, Chi-Yu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/vm36r3

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 107 === As the IEEE 802.11ac becomes the mainstream Wi-Fi standard which introduces several new features, the number of available rate options increases. % due to its new channel bonding and modulation schemes. It challenges the scalability of conventional rate adaptations (RAs). It is because their designs are based on the old rate scope; moreover, many of them are incompliant to commodity Wi-Fi NICs. Our case study shows that two popular 802.11ac RAs, Minstrel-HT and Iwlwifi, fall short of expected performance in some cases due to their non-scalable designs. We thus propose a scalable, intelligent 802.11ac RA solution, called DRL-RA, which takes a deep reinforcement learning (DRL) based approach. The DRL model can guide the RA to reach the best rate by suggesting candidate rates for its probing process based on real-time channel estimation. The key insight is that the model can automatically adapt to environments, and identify a path to the best rate by learning the correlations between rate features, performance, link quality, and channel utilization rate. Its suggested rates are concentrated and precise, thereby being able to locate the best rates with low overhead. We prototype DRL-RA using the Intel NIC driver and TensorFlow with an asynchronous framework across kernel and user spaces. Our experiments show that DRL-RA outperforms the other popular RAs by up to 2.8 times.