• Technical Name
  • 基於深度強化學習,智慧化商用Wi-Fi裝置增強通訊效能
  • Operator
  • National Yang Ming Chiao Tung University
  • Booth
  • AIoT&智慧應用 AIoT & Smart Applications
  • Contact
  • 李奇育
  • Email
  • chiyuli@cs.nctu.edu.tw
Technical Description We develop an asynchronous framework across userkernel spaces for deep learning applications on the improvement of Wi-Fi performanceimplement it in the driver of commodity Intel Wi-Fi cards. Under this framework, we apply deep reinforcement learning to developing an intelligent rate adaptation (RA) algorithm (DRL-RA), which can achieve the highest throughput in varying channel conditions given many rate options of current Wi-Fi technologies. Its on-learning capability can learn how to efficiently approach the best rate from the experiences of its common usage patternenvironment.
Scientific Breakthrough \"1. We developed the first asynchronous framework across userkernel spaces for deep learning applications on the Wi-Fi performance,implemented it in the driver of commodity Intel Wi-Fi cards by using Google Tensorflow.2. We designed an intelligent, practical RA solution (DRL-RA), whose online learning capability can automatically derive low-overhead paths to approach the best rates to achieve the highest throughput over time in various channel conditions.3. DRL-RA can outperform Intel IwlwifiLinux default Minstrel by up to 821.4242.8, respectively.\"
Industrial Applicability The developed technology can be applied to the network communication industry, especially for the vendors of Wi-Fi APNIC. It mainly consists of two techniques. First, the deep learning application framework for kernel modules can allow the vendors to enable intelligent services on their wirelessnetworking system products by applying AI technique. Second, the deep reinforcement learning based rate adaptation solution can enhance the Wi-Fi performance for Wi-Fi chipequipment vendors. Its design architecture with the use of Google Tensorflow can be also applied to other functions.
本網站使用您的Cookie於優化網站。繼續瀏覽網站即表示您同意本公司隱私權政策,您可至隱私權政策了解詳細資訊。