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

Indoor localization determines an objects 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 design...

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Bibliographic Details
Authors: Castillo Cara, Manuel, Martínez Gómez, Jesús, Ballesteros Jerez, Javier, García Varea, Ismael, García Castro, Raúl, Orozco Barbosa, Luis
Format: article
Publication Date:2025
Country:España
Institution:Universidad de Castilla-La Mancha
Repository:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/43595
Online Access:https://ieeexplore.ieee.org/document/10946146
https://hdl.handle.net/10578/43595
Access Level:Open access
Keyword:Deep learning
Hybrid neural network
Indoor localisation
Massive MIMO
Positioning
Synthetic images
Description
Summary:Indoor localization determines an objects 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.

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