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Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)

Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adj...

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Autores principales: Al-Jumaily, Abdulmajeed, Sali, Aduwati, Jiménez, Víctor P. Gil, Lagunas, Eva, Natrah, Fatin Mohd Ikhsan, Fontán, Fernando Pérez, Hussein, Yaseein Soubhi, Singh, Mandeep Jit, Samat, Fazdliana, Aljumaily, Harith, Al-Jumeily, Dhiya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346904/
https://www.ncbi.nlm.nih.gov/pubmed/37448024
http://dx.doi.org/10.3390/s23136175
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author Al-Jumaily, Abdulmajeed
Sali, Aduwati
Jiménez, Víctor P. Gil
Lagunas, Eva
Natrah, Fatin Mohd Ikhsan
Fontán, Fernando Pérez
Hussein, Yaseein Soubhi
Singh, Mandeep Jit
Samat, Fazdliana
Aljumaily, Harith
Al-Jumeily, Dhiya
author_facet Al-Jumaily, Abdulmajeed
Sali, Aduwati
Jiménez, Víctor P. Gil
Lagunas, Eva
Natrah, Fatin Mohd Ikhsan
Fontán, Fernando Pérez
Hussein, Yaseein Soubhi
Singh, Mandeep Jit
Samat, Fazdliana
Aljumaily, Harith
Al-Jumeily, Dhiya
author_sort Al-Jumaily, Abdulmajeed
collection PubMed
description Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques.
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spelling pubmed-103469042023-07-15 Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS) Al-Jumaily, Abdulmajeed Sali, Aduwati Jiménez, Víctor P. Gil Lagunas, Eva Natrah, Fatin Mohd Ikhsan Fontán, Fernando Pérez Hussein, Yaseein Soubhi Singh, Mandeep Jit Samat, Fazdliana Aljumaily, Harith Al-Jumeily, Dhiya Sensors (Basel) Article Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques. MDPI 2023-07-05 /pmc/articles/PMC10346904/ /pubmed/37448024 http://dx.doi.org/10.3390/s23136175 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Al-Jumaily, Abdulmajeed
Sali, Aduwati
Jiménez, Víctor P. Gil
Lagunas, Eva
Natrah, Fatin Mohd Ikhsan
Fontán, Fernando Pérez
Hussein, Yaseein Soubhi
Singh, Mandeep Jit
Samat, Fazdliana
Aljumaily, Harith
Al-Jumeily, Dhiya
Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title_full Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title_fullStr Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title_full_unstemmed Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title_short Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)
title_sort evaluation of 5g and fixed-satellite service earth station (fss-es) downlink interference based on artificial neural network learning models (ann-lms)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346904/
https://www.ncbi.nlm.nih.gov/pubmed/37448024
http://dx.doi.org/10.3390/s23136175
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