Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

doi:10.1109/MCOM.2019.1800635

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	Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities
	Yang Yang1 ; Yang Li 2; Wuxiong Zhang 3; Fei Qin 4; Pengcheng Zhu 5; Cheng-Xiang Wang 5
	2019-03
发表期刊	IEEE COMMUNICATIONS MAGAZINE
ISSN	0163-6804
卷号	57 期号:3 页码:22-27
发表状态	已发表
DOI	10.1109/MCOM.2019.1800635
摘要	In modern wireless communication systems, wireless channel modeling has always been a fundamental task in system design and performance optimization. Traditional channel modeling methods, such as ray-tracing and geometry-based stochastic channel models, require in-depth domain-specific knowledge and technical expertise in radio signal propagations across electromagnetic fields. To avoid these difficulties and complexities, a novel generative adversarial network (GAN) framework is proposed for the first time to address the problem of autonomous wireless channel modeling without complex theoretical analysis or data processing. Specifically, the GAN is trained by raw measurement data to reach the Nash equilibrium of a MinMax game between a channel data generator and a channel data discriminator. Once this process converges, the resulting channel data generator is extracted as the target channel model for a specific application scenario. To demonstrate, the distribution of a typical additive white Gaussian noise channel is successfully approximated by using the proposed GAN-based channel modeling framework, thus verifying its good performance and effectiveness.
关键词	Wireless communication Generators Channel models Artificial intelligence Analytical models Data models MIMO communication
URL	查看原文
收录类别	SCI ; SCIE ; EI
语种	英语
资助项目	EU[734325]
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000461239300004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20191206648358
EI主题词	Data handling ; Decoding ; Electromagnetic fields ; Stochastic models ; Stochastic systems ; White noise
EI分类号	Electricity and Magnetism:701 ; Data Processing and Image Processing:723.2 ; Probability Theory:922.1 ; Systems Science:961
原始文献类型	Article
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/30587
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.ShanghaiTech University 2.Shanghai Research Center for Wireless Communications 3.Chinese Academy of Sciences 4.University of Chinese Academy of Sciences 5.Southeast University
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Yang Yang,Yang Li,Wuxiong Zhang,et al. Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities[J]. IEEE COMMUNICATIONS MAGAZINE,2019,57(3):22-27.
APA	Yang Yang,Yang Li,Wuxiong Zhang,Fei Qin,Pengcheng Zhu,&Cheng-Xiang Wang.(2019).Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities.IEEE COMMUNICATIONS MAGAZINE,57(3),22-27.
MLA	Yang Yang,et al."Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities".IEEE COMMUNICATIONS MAGAZINE 57.3(2019):22-27.