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
Autores principales: Kong, Lingjin, Zhang, Xiaoying, Zhao, Haitao, Wei, Jibo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534843/
https://www.ncbi.nlm.nih.gov/pubmed/34681992
http://dx.doi.org/10.3390/e23101268

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534843/
https://www.ncbi.nlm.nih.gov/pubmed/34681992
http://dx.doi.org/10.3390/e23101268

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