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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 |
ISSN | 1551-3203 |
EISSN | 1941-0050 |
卷号 | 18期号:4页码:2603-2613 |
发表状态 | 已发表 |
DOI | 10.1109/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 |
URL | 查看原文 |
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 |
引用统计 | |
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 |
推荐引用方式 GB/T 7714 | 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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