Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning...

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Detalles Bibliográficos
Autores principales: Essai Ali, Mohamed Hassan, Taha, Ibrahim B.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409333/
https://www.ncbi.nlm.nih.gov/pubmed/34541310
http://dx.doi.org/10.7717/peerj-cs.682

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409333/
https://www.ncbi.nlm.nih.gov/pubmed/34541310
http://dx.doi.org/10.7717/peerj-cs.682

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