Open Access

ARTICLE

Cross-Site Map-Free Indoor Localization for 6G ISAC Systems Using Low-Frequency Radio and Transformer Networks

Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

1 Department of Mechanical Engineering, Kanagawa University, Yokohama, Kanagawa, 221-8686, Japan
2 Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City, 24301, Taiwan
3 Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, 24301, Taiwan
4 Research Center for Intelligent Medical Devices, Ming Chi University of Technology, New Taipei City, 24301, Taiwan

* Corresponding Author: En-Cheng Liou. Email:

(This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)

Computer Modeling in Engineering & Sciences 2025, 145(2), 2551-2571. https://doi.org/10.32604/cmes.2025.072471

Received 27 August 2025; Accepted 03 November 2025; Issue published 26 November 2025

Abstract

Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate localization in unseen environments. Evaluation across two validation sites demonstrates the framework’s effectiveness. In cross-site testing (Site-A), the Transformer achieved a mean localization error of 9.44 m, outperforming the Deep Neural Network (DNN) (10.76 m) and Convolutional Neural Network (CNN) (12.02 m) baselines. In a real-time deployment (Site-B) spanning three floors, the Transformer maintained an overall mean error of 9.81 m, compared with 13.45 m for DNN, 12.88 m for CNN, and 53.08 m for conventional trilateration. For vertical positioning, the Transformer delivered a mean error of 4.52 m, exceeding the performance of DNN (4.59 m), CNN (4.87 m), and trilateration (>45 m). The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration, providing stable, sub-12 m horizontal accuracy and reliable vertical estimation. This capability makes the framework suitable for real-time applications in smart buildings, emergency response, and autonomous systems. By utilizing multipath reflections as an informative structure rather than treating them as noise, this work advances artificial intelligence (AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.

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Keywords

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