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

2025-04-24

AI Summary

To address poor generalizability, scarce labeled data, and high measurement heterogeneity in CSI-based indoor localization under large-scale ISAC deployments, this paper introduces the first ultra-large-scale (400+ APs) WiFi CSI localization system designed for real-world building environments. Methodologically, we propose: (1) a graph-structured representation to model heterogeneous CSI signals; (2) a self-supervised pretraining framework incorporating spatiotemporal priors; and (3) a confidence-aware fine-tuning strategy to enhance cross-device robustness. Built upon a graph neural network and a server-side end-to-end learning architecture, our system achieves a median localization error of 2.17 m and 99.49% floor-level accuracy across a five-story, 25,600 m² real-world deployment. The mean absolute error is reduced by 18.7% over the best baseline, significantly advancing the practicality and scalability of CSI-based indoor localization.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Deploying CSI-based localization at large scale in real-world buildings
• Addressing underutilized unlabeled data and CSI measurement heterogeneity
• Improving WiFi localization accuracy for large-scale ISAC deployments

Innovation

• Graph-based structure models heterogeneous CSI data
• Pretext pretraining leverages unlabeled CSI data
• Confidence-aware fine-tuning enhances localization robustness