Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio ch...

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Autores principales:	Krzysztof K. Cwalina, Piotr Rajchowski, Alicja Olejniczak, Olga Błaszkiewicz, Robert Burczyk
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning heterogeneous network channel state LTE Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/217152518b9a45b3ae85889bdbeac016
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spelling	oai:doaj.org-article:217152518b9a45b3ae85889bdbeac0162021-11-25T18:58:40ZChannel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning10.3390/s212277161424-8220https://doaj.org/article/217152518b9a45b3ae85889bdbeac0162021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7716https://doaj.org/toc/1424-8220Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.Krzysztof K. CwalinaPiotr RajchowskiAlicja OlejniczakOlga BłaszkiewiczRobert BurczykMDPI AGarticledeep learningheterogeneous networkchannel stateLTEChemical technologyTP1-1185ENSensors, Vol 21, Iss 7716, p 7716 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning heterogeneous network channel state LTE Chemical technology TP1-1185
spellingShingle	deep learning heterogeneous network channel state LTE Chemical technology TP1-1185 Krzysztof K. Cwalina Piotr Rajchowski Alicja Olejniczak Olga Błaszkiewicz Robert Burczyk Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
description	Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.
format	article
author	Krzysztof K. Cwalina Piotr Rajchowski Alicja Olejniczak Olga Błaszkiewicz Robert Burczyk
author_facet	Krzysztof K. Cwalina Piotr Rajchowski Alicja Olejniczak Olga Błaszkiewicz Robert Burczyk
author_sort	Krzysztof K. Cwalina
title	Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
title_short	Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
title_full	Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
title_fullStr	Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
title_full_unstemmed	Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
title_sort	channel state estimation in lte-based heterogenous networks using deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/217152518b9a45b3ae85889bdbeac016
work_keys_str_mv	AT krzysztofkcwalina channelstateestimationinltebasedheterogenousnetworksusingdeeplearning AT piotrrajchowski channelstateestimationinltebasedheterogenousnetworksusingdeeplearning AT alicjaolejniczak channelstateestimationinltebasedheterogenousnetworksusingdeeplearning AT olgabłaszkiewicz channelstateestimationinltebasedheterogenousnetworksusingdeeplearning AT robertburczyk channelstateestimationinltebasedheterogenousnetworksusingdeeplearning
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

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