Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning

doi:10.1109/ACCESS.2020.2979220

	Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning
	Zhang, Jiachi 1,2; Liu, Liu 1,3; Fan, Yuanyuan 1; Zhuang, Lingfan 1; Zhou, Tao 1,4; Piao, Zheyan 2
	2020
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	8 页码:47797-47806
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%.
关键词	Wireless channel scenarios identification machine learning QuaDRiGa platform high-speed railway scenarios
DOI	10.1109/ACCESS.2020.2979220
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000524679300008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：30[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14239
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.