Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel

Lu, Guobao, Dai, Xuewu, Zhang, Wuxiong, Yang, Yang and Qin, Fei (2022) Nondata-Aided Rician Parameters Estimation With Redundant GMM for Adaptive Modulation in Industrial Fading Channel. IEEE Transactions on Industrial Informatics, 18 (4). pp. 2603-2613. ISSN 1551-3203

QinF_Lu2022_TII_(AccpetedVersion)_Nondata-Aided_Rician_Parameters_Estimation_With_Redundant_GMM_for_Adaptive_Modulation_in_Industrial_Fading_Channel.pdf - Accepted Version

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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.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the Nature Science Foundation of China under Grant 62071450, in part by Heilongjiang Provincial Key Science and Technology Project under Grant 2020ZX03A02, in part by the Scientific Instrument Developing Project of the Chinese Academy of Sciences under Grant YJKYYQ20170074, in part by the Fundamental Research Funds for the Central Universities, and in part by the National Key Research and Development Program of China under Grant 2019YFB2101602 and Grant 2020YFB2104300
Uncontrolled Keywords: Rician Parameters, Maximum Likelihood Estimation, Non-data Aided, Gaussian Mixture Model, Convergence
Subjects: F300 Physics
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 21 Jan 2022 12:01
Last Modified: 21 Jan 2022 12:15

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