Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel
Guobao Lu1; Xuewu Dai2; Wuxiong Zhang3; Yang Yang4; Fei Qin1
2022-04-01
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
EISSN1941-0050
卷号18期号:4页码:2603-2613
发表状态已发表
DOI10.1109/TII.2021.3095253
摘要Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requires a priori aiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.
关键词Rician channels Fading channels Estimation Modulation Wireless communication Parameter estimation Informatics Convergence Gaussian mixture model maximum likelihood estimation nondata aided Rician parameters
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收录类别SCI ; EI ; SCIE
语种英语
资助项目Nature Science Foundation of China[62071450] ; Heilongjiang Provincial Key Science and Technology Project[2020ZX03A02] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20170074] ; National Key Research and Development Program of China["2019YFB2101602","2020YFB2104300"]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
WOS类目Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号WOS:000739636900045
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Scopus 记录号2-s2.0-85122573623
来源库Scopus
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/148748
专题信息科学与技术学院_PI研究组_杨旸组
作者单位1.School of Electronic and Electrical Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
2.Department of Mathematics, Physics, and Electrical Engineering, Northumbria University, Newcastle-upon-Tyne, U.K.
3.Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai, China
4.Shanghai Institute of Fog Computing Technology (SHIFT), ShanghaiTech University, Shanghai, China
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Guobao Lu,Xuewu Dai,Wuxiong Zhang,et al. Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2022,18(4):2603-2613.
APA Guobao Lu,Xuewu Dai,Wuxiong Zhang,Yang Yang,&Fei Qin.(2022).Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,18(4),2603-2613.
MLA Guobao Lu,et al."Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 18.4(2022):2603-2613.
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