A Novel Deep Learning Method for Application Identification in Wireless Network

作     者：Jie Ren Zulin Wang Jie Ren;Zulin Wang;School of Electronic and Information Engineering, Beihang University;Collaborative Innovation Center of Geospatial Technology

作者机构：School of Electronic and Information Engineering Beihang University Beijing 100191 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2018年第15卷第10期

页      面：73-83页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by NSAF under Grant(No.U1530117) National Natural Science Foundation of China(No.61471022 and No.61201156) 

主　　题：quality of experience application identification protocol identification deeplearning feature extraction 

摘      要：In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.