@Article{cmes.2025.072471,
AUTHOR = {Bin Zhang, En-Cheng Liou, Yi-Chih Tung, Muhammad Usman, Chiung-An Chen, Chao-Shun Yang},
TITLE = {Cross-Site Map-Free Indoor Localization for 6G ISAC Systems Using Low-Frequency Radio and Transformer Networks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {2},
PAGES = {2551--2571},
URL = {http://www.techscience.com/CMES/v145n2/64586},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.072471}
}