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Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks
The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. T...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697607/ https://www.ncbi.nlm.nih.gov/pubmed/33198421 http://dx.doi.org/10.3390/s20226475 |
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author | Pecorella, Tommaso Fantacci, Romano Picano, Benedetta |
author_facet | Pecorella, Tommaso Fantacci, Romano Picano, Benedetta |
author_sort | Pecorella, Tommaso |
collection | PubMed |
description | The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem. |
format | Online Article Text |
id | pubmed-7697607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76976072020-11-29 Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks Pecorella, Tommaso Fantacci, Romano Picano, Benedetta Sensors (Basel) Article The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem. MDPI 2020-11-12 /pmc/articles/PMC7697607/ /pubmed/33198421 http://dx.doi.org/10.3390/s20226475 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pecorella, Tommaso Fantacci, Romano Picano, Benedetta Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title | Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title_full | Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title_fullStr | Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title_full_unstemmed | Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title_short | Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks |
title_sort | improving csi prediction accuracy with deep echo state networks in 5g networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697607/ https://www.ncbi.nlm.nih.gov/pubmed/33198421 http://dx.doi.org/10.3390/s20226475 |
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