Abstract
5G supports more new services, including enhanced Mobile Broadband (eMBB), Ultra-reliable and Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC). The Quality of Service (QoS) requirements of these 5G service types are different. In this paper, we capture the packets from the Narrow Band-Internet of Things (NB-IoT) transmission, Unmanned Aerial Vehicle (UAV) control, 4K video and Facebook access for emulating mMTC, URLLC, eMBB and Internet traffic in 5G. With the captured packets, we investigate using the machine learning technology to classify the packets based on the payload information. Specifically, the Convolutional Neural Network (CNN) model is performed to classify the application packets into suitable groups. In addition, this paper studies the effects of various parameters such as the kernel number, kernel size, pooling window size, the dropout rate and the payload length to find the optimal values for high accuracy and low latency.
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