AHCI RESEARCH GROUP

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proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Ozeki, R.; Yonekura, H.; Rizk, H.; Yamaguchi, H.

Cellular-based Indoor Localization with Adapted LLM and Label-aware Contrastive Learning Proceedings Article

In: pp. 138–145, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331586461 (ISBN).

Abstract | Links | BibTeX | Tags: Cellular Network, Cellulars, Computer interaction, Contrastive Learning, Deep learning, Human computer interaction, Indoor Localization, Indoor Navigation, Indoor positioning, Indoor positioning systems, Language Model, Large language model, Learning systems, Mobile computing, Mobile-computing, Signal processing, Smart Environment, Wireless networks

@inproceedings{ozeki_cellular-based_2025,

title = {Cellular-based Indoor Localization with Adapted LLM and Label-aware Contrastive Learning},

author = {R. Ozeki and H. Yonekura and H. Rizk and H. Yamaguchi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010820397&doi=10.1109%2FSMARTCOMP65954.2025.00070&partnerID=40&md5=9e15d9f4225f00cd57bedc511aad27d9},

doi = {10.1109/SMARTCOMP65954.2025.00070},

isbn = {9798331586461 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {138–145},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Accurate indoor positioning is essential for mobile computing, human-computer interaction, and next-generation smart environments, enabling applications in indoor navigation, augmented reality, personalized services, healthcare, and emergency response. Cellular signal fingerprinting has emerged as a widely adopted solution, with deep learning models achieving state-of-the-art performance. However, existing approaches face critical deployment challenges, including labor-intensive fingerprinting, sparse reference points, and missing RSS values caused by environmental interference, hardware variability, and dynamic signal fluctuations. These limitations hinder their scalability, adaptability, and real-world usability in complex indoor environments. To address these challenges, we present GPT2Loc a novel indoor localization framework that integrates LLM with label-aware contrastive learning, improving accuracy while reducing reliance on extensive fingerprinting. LLMs effectively extract meaningful spatial features from incomplete and noisy RSS data, enabling robust localization even in sparsely finger-printed areas. Our label-aware contrastive learning approach further enhances generalization by aligning latent representations with spatial relationships, allowing GPT2Loc to interpolate user locations in unseen areas and mitigate signal inconsistencies. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Cellular Network, Cellulars, Computer interaction, Contrastive Learning, Deep learning, Human computer interaction, Indoor Localization, Indoor Navigation, Indoor positioning, Indoor positioning systems, Language Model, Large language model, Learning systems, Mobile computing, Mobile-computing, Signal processing, Smart Environment, Wireless networks},

pubstate = {published},

tppubtype = {inproceedings}

}