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Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning

文献类型：期刊论文

作者Zhang, Jiachi1,2; Liu, Liu1,3; Fan, Yuanyuan1; Zhuang, Lingfan1; Zhou, Tao1,4; Piao, Zheyan2
刊名IEEE ACCESS
出版日期2020
卷号8页码:47797-47806
关键词Wireless channel scenarios identification machine learning QuaDRiGa platform high-speed railway scenarios
ISSN号2169-3536
DOI10.1109/ACCESS.2020.2979220
英文摘要Wireless channel scenarios identification is of pivotal significance for dedicated wireless communication design, especially for the heterogeneous network covering rich propagation environments. In this paper, the identification problem is investigated by machine learning approaches. To enhance the identification performance, some preprocessing methods, mainly referring to the data normalization and dimension reduction, are adopted. Then, both supervised and unsupervised learning algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), k-means, and Gaussian mixture model (GMM) are used to realize the scenarios identification, respectively. Finally, the identification performance of these four approaches are validated both on the actual measured HSR wireless channel data sets and the QuaDRiGa channel emulation platform with the ability of multiple scenarios emulation. Most of the results indicate that k-NN and SVM approaches can achieve an accuracy over 90%. As for those two unsupervised learning approaches, the GMM proves to be a promising approach by presenting a performance close to the former two approaches without training process, whereas the k-means yields an accuracy about 80%.
资助项目Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund[L172030] ; National Natural Science Foundation of China[61701017] ; Center of National Railway Intelligent Transportation System Engineering and Technology, China Academy of Railway Sciences[RITS2019KF01]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000524679300008
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/14238]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liu, Liu
作者单位1.Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
2.Shandong Jiaotong Univ, Sch Rail Transportat, Jinan 250357, Peoples R China
3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China
4.China Acad Railway Sci, Ctr Natl Railway Intelligent Transportat Syst Eng, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jiachi,Liu, Liu,Fan, Yuanyuan,et al. Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning[J]. IEEE ACCESS,2020,8:47797-47806.
APA Zhang, Jiachi,Liu, Liu,Fan, Yuanyuan,Zhuang, Lingfan,Zhou, Tao,&Piao, Zheyan.(2020).Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning.IEEE ACCESS,8,47797-47806.
MLA Zhang, Jiachi,et al."Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning".IEEE ACCESS 8(2020):47797-47806.

入库方式： OAI收割

来源：计算技术研究所

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