Non-Data Aided Rician Parameters Estimation in Temporal Fading Channel With 3 DoFs Gaussian Mixture Model

Zhang, Qilong, Lu, Guobao, Zhang, Wuxiong, Shen, Fei, Dai, Xuewu, Jiao, Jianbin and Qin, Fei (2019) Non-Data Aided Rician Parameters Estimation in Temporal Fading Channel With 3 DoFs Gaussian Mixture Model. IEEE Access, 7. pp. 62198-62209. ISSN 2169-3536

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Rician distribution has been widely utilized to describe wireless fading channel. In the non-stationary temporal fading channel like industrial scenarios, both the specular and scattered components of the multi-path fading channel will be time varying. As a result, the online estimation of Rician parameters is necessary to provide stable wireless service. The traditional estimation approaches of Rician parameters are designed for channel measurement usage and therefore have to work in the data-aided mode for online estimation with modulated I/Q samples. To solve this problem, some non-data-aided algorithms have been proposed in recent years, but only valid in specific scenarios. In this paper, we formulate the estimation of Rician parameters from modulated I/Q samples as a two-dimensional Gaussian mixture model to provide a general non-data-aided Rician parameter estimation method. By involving a priori information of modulation scheme and the motivation of optimized gradient searching, the independent parameters in the maximum likelihood estimation can be significantly decreased to three, which leads to fast convergence of the modified expectation-maximization algorithm with high accuracy. The combination of these modifications has been finally formulated as a Rician mixture model. The numerical results and field measurements illustrate the feasibility of this methodology.

Item Type: Article
Uncontrolled Keywords: Rician distribution, maximum likelihood estimation, non-data aided, Gaussian mixture model
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 13 Jun 2019 10:08
Last Modified: 01 Aug 2021 11:31

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