Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform

• URL: http://arxiv.org/abs/2504.17173v1
• Date: Thu, 24 Apr 2025 01:16:40 GMT
• Title: Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform
• Authors: Tianyu Zhang, Dongheng Zhang, Ruixu Geng, Xuecheng Xie, Shuai Yang, Yan Chen,
• Abstract summary: We explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm.<n>We propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side.
• Score: 18.4186280784439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600 m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.

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