Castillo-Cara, ManuelMartínez-Gómez, JesúsBallesteros-Jerez, JavierGarcía-Varea, IsmaelGarcía-Castro, RaúlOrozco-Barbosa, Luis2025-10-142025-10-142025-03-31M. Castillo-Cara, J. Martínez-Gómez, J. Ballesteros-Jerez, I. García-Varea, R. García-Castro and L. Orozco-Barbosa, "MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning," in IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 3, pp. 559-571, April 20251941-0484https://doi.org/10.1109/JSTSP.2025.3555067https://hdl.handle.net/20.500.14468/30409The registered version of this article, first published in IEEE Journal of Selected Topics in Signal Processing, is available online at the publisher's website: Institute of Electrical and Electronics Engineers (IEEE), https://doi.org/10.1109/JSTSP.2025.3555067La versión registrada de este artículo, publicado por primera vez en IEEE Journal of Selected Topics in Signal Processing, está disponible en línea en el sitio web del editor: Institute of Electrical and Electronics Engineers (IEEE), https://doi.org/10.1109/JSTSP.2025.3555067This work was supported in part by the Government of Castilla-La Mancha and “ERDF A way of making Europe” under Project SBPLY/21/180225/000062, in part by CYTED under Grant 520rt0011, in part by the Spanish Government (AEI) under Projects PID2022-137344OB-C33 /AEI/10.13039/501100011033, Project PID2021-123627OB-C52, and Project PID2019–106758GB–C33, in part by Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with the Universidad Politécnica de Madrid in the Excellence Programme for University Teaching Staff, in the context of the V PRICIT (Regional Programme of Research and TechnologicalInnovation),andinpartbytheUniversidaddeCastilla-LaMancha and “ERDF A way of making Europe” under Project 2022-GRIN-34437.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.eninfo:eu-repo/semantics/openAccess1203.04 Inteligencia artificialartículomassive MIMOdeep learninghybrid neural networksynthetic imagespositioningindoor localisation