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

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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Bibliographic Details
Main Authors: Lingjin Kong, Xiaoying Zhang, Haitao Zhao, Jibo Wei
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Entropy
Subjects:
channel measurement
Gaussian mixture model
variational Bayesian
sparse channel
channel estimation
Online Access:https://www.mdpi.com/1099-4300/23/10/1268

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