MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning

Indoor localization determines an object’s position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially desig...

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Autores: Castillo-Cara, Manuel, Martínez-Gómez, Jesús, Ballesteros-Jerez, Javier, García-Varea, Ismael, García-Castro, Raúl, Orozco-Barbosa, Luis
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositório:e-spacio. Repositorio Institucional de la UNED
Idioma:inglês
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/30409
Acesso em linha:https://hdl.handle.net/20.500.14468/30409
Access Level:Acceso aberto
Palavra-chave:1203.04 Inteligencia artificial
massive MIMO
deep learning
hybrid neural network
synthetic images
positioning
indoor localisation
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spelling MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep LearningCastillo-Cara, ManuelMartínez-Gómez, JesúsBallesteros-Jerez, JavierGarcía-Varea, IsmaelGarcía-Castro, RaúlOrozco-Barbosa, Luis1203.04 Inteligencia artificialmassive MIMOdeep learninghybrid neural networksynthetic imagespositioningindoor localisationIndoor localization determines an object’s position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key in indoor positioning due to its spatial diversity and multipath propagation. This study integrates MIMO-based indoor localization with Hybrid Neural Networks (HyNN), converting structured datasets into synthetic images using TINTO. This research marks the first application of HyNNs using synthetic images for MIMO-based indoor localization. Our key contributions include: (i) adapting TINTO for regression problems; (ii) using synthetic images as input data for our model; (iii) designing a novel HyNN with a Convolutional Neural Network branch for synthetic images and an MultiLayer Percetron branch for tidy data; and (iv) demonstrating improved results and metrics compared to prior literature. These advancements highlight the potential of HyNNs in enhancing the accuracy and efficiency of indoor localization systems.Institute of Electrical and Electronics Engineers (IEEE)e-Spacio UNED20252025-10-1420252025-03-3120252025-03-31journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/30409reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/304092026-06-06T12:38:31Z
dc.title.none.fl_str_mv MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
title MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
spellingShingle MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
Castillo-Cara, Manuel
1203.04 Inteligencia artificial
massive MIMO
deep learning
hybrid neural network
synthetic images
positioning
indoor localisation
title_short MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
title_full MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
title_fullStr MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
title_full_unstemmed MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
title_sort MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning
dc.creator.none.fl_str_mv Castillo-Cara, Manuel
Martínez-Gómez, Jesús
Ballesteros-Jerez, Javier
García-Varea, Ismael
García-Castro, Raúl
Orozco-Barbosa, Luis
author Castillo-Cara, Manuel
author_facet Castillo-Cara, Manuel
Martínez-Gómez, Jesús
Ballesteros-Jerez, Javier
García-Varea, Ismael
García-Castro, Raúl
Orozco-Barbosa, Luis
author_role author
author2 Martínez-Gómez, Jesús
Ballesteros-Jerez, Javier
García-Varea, Ismael
García-Castro, Raúl
Orozco-Barbosa, Luis
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv 1203.04 Inteligencia artificial
massive MIMO
deep learning
hybrid neural network
synthetic images
positioning
indoor localisation
topic 1203.04 Inteligencia artificial
massive MIMO
deep learning
hybrid neural network
synthetic images
positioning
indoor localisation
description Indoor localization determines an object’s position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key in indoor positioning due to its spatial diversity and multipath propagation. This study integrates MIMO-based indoor localization with Hybrid Neural Networks (HyNN), converting structured datasets into synthetic images using TINTO. This research marks the first application of HyNNs using synthetic images for MIMO-based indoor localization. Our key contributions include: (i) adapting TINTO for regression problems; (ii) using synthetic images as input data for our model; (iii) designing a novel HyNN with a Convolutional Neural Network branch for synthetic images and an MultiLayer Percetron branch for tidy data; and (iv) demonstrating improved results and metrics compared to prior literature. These advancements highlight the potential of HyNNs in enhancing the accuracy and efficiency of indoor localization systems.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-10-14
2025
2025-03-31
2025
2025-03-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/30409
url https://hdl.handle.net/20.500.14468/30409
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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