Variational Sparse Bayesian Learning for Estimation of Gaussian Mixture Distributed Wireless Channels

In this paper, variational sparse Bayesian learning is utilized to estimate the multipath parameters for wireless channels. Due to its flexibility to fit any probability density function (PDF), the Gaussian mixture model (GMM) is introduced to represent the complicated fading phenomena in various co...

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Detalles Bibliográficos

Principais autores:	Lingjin Kong, Xiaoying Zhang, Haitao Zhao, Jibo Wei
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2021-09-01
Series:	Entropy
Assuntos:	channel measurement Gaussian mixture model variational Bayesian sparse channel channel estimation
Acceso en liña:	https://www.mdpi.com/1099-4300/23/10/1268
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