Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Essai Ali, Mohamed Hassan, Taha, Ibrahim B.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
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
_version_ 1783746976867680256
author Essai Ali, Mohamed Hassan
Taha, Ibrahim B.M.
author_facet Essai Ali, Mohamed Hassan
Taha, Ibrahim B.M.
author_sort Essai Ali, Mohamed Hassan
collection PubMed
description 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 approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.
format Online
Article
Text
id pubmed-8409333
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-84093332021-09-17 Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach Essai Ali, Mohamed Hassan Taha, Ibrahim B.M. PeerJ Comput Sci Artificial Intelligence 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 approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system. PeerJ Inc. 2021-08-26 /pmc/articles/PMC8409333/ /pubmed/34541310 http://dx.doi.org/10.7717/peerj-cs.682 Text en ©2021 Essai Ali and Taha https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Essai Ali, Mohamed Hassan
Taha, Ibrahim B.M.
Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title_full Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title_fullStr Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title_full_unstemmed Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title_short Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach
title_sort channel state information estimation for 5g wireless communication systems: recurrent neural networks approach
topic Artificial Intelligence
url 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
work_keys_str_mv AT essaialimohamedhassan channelstateinformationestimationfor5gwirelesscommunicationsystemsrecurrentneuralnetworksapproach
AT tahaibrahimbm channelstateinformationestimationfor5gwirelesscommunicationsystemsrecurrentneuralnetworksapproach