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.

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 to821.4242.8, respectively.

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 toother functions.