A Big Data Enabled Channel Model for 5G Wireless Communication Systems

doi:10.1109/TBDATA.2018.2884489

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	A Big Data Enabled Channel Model for 5G Wireless Communication Systems
	Jie Huang 1; Cheng-Xiang Wang 1; Lu Bai 2; Jian Sun 3; Yang Yang4 ; Jie Li 5; Olav Tirkkonen 6; Ming-Tuo Zhou 7
	2020
发表期刊	IEEE TRANSACTIONS ON BIG DATA
ISSN	23327790
卷号	6 期号:2 页码:211-222
发表状态	已发表
DOI	10.1109/TBDATA.2018.2884489
摘要	The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling. © 2015 IEEE.
关键词	Big data wireless communications machine learning channel modeling artificial neural network
URL	查看原文
收录类别	EI ; SCIE ; ESCI
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20185006231523
EI主题词	Antennas ; Big data ; Data Analytics ; Data communication systems ; Large dataset ; Learning algorithms ; Learning systems ; Machine learning ; Neural networks ; Radial basis function networks ; Stochastic models ; Stochastic systems
EI分类号	Data Processing and Image Processing:723.2 ; Probability Theory:922.1 ; Systems Science:961
原始文献类型	Journal article (JA)
来源库	IEEE
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/121582
专题	科道书院信息科学与技术学院_PI研究组_杨旸组
作者单位	1.National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China 2.School of Cyber Science and Technology, Beihang University, Beijing, China 3.Shandong Provincial Key Lab of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University, Qingdao, China 4.Shanghai Institute of Fog Computing Technology (SHIFT), SCA, ShanghaiTech University, Shanghai, China 5.Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China 6.Aalto University, Espoo, Finland 7.Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences (CAS), Shanghai, China
推荐引用方式 GB/T 7714	Jie Huang,Cheng-Xiang Wang,Lu Bai,et al. A Big Data Enabled Channel Model for 5G Wireless Communication Systems[J]. IEEE TRANSACTIONS ON BIG DATA,2020,6(2):211-222.
APA	Jie Huang.,Cheng-Xiang Wang.,Lu Bai.,Jian Sun.,Yang Yang.,...&Ming-Tuo Zhou.(2020).A Big Data Enabled Channel Model for 5G Wireless Communication Systems.IEEE TRANSACTIONS ON BIG DATA,6(2),211-222.
MLA	Jie Huang,et al."A Big Data Enabled Channel Model for 5G Wireless Communication Systems".IEEE TRANSACTIONS ON BIG DATA 6.2(2020):211-222.