A Retrieval-Assisted Framework for Wireless Localization
Haoyu Huang, Guangjin Pan, Kaixuan Huang, Shunqing Zhang, Yuhao Zhang, Musa Furkan Keskin, Zheng Xing, Henk Wymeersch

TL;DR
This paper introduces a retrieval-assisted wireless localization framework that combines similarity-based and learning-based methods, employing channel charting and graph attention networks to improve accuracy and scalability in high-dimensional CSI spaces.
Contribution
It proposes a unified framework integrating channel charting and graph attention networks to enhance wireless localization accuracy and efficiency.
Findings
Outperforms state-of-the-art methods in indoor scenarios
Demonstrates scalability in high-dimensional CSI spaces
Achieves higher localization accuracy in real-world tests
Abstract
Accurate and robust wireless localization is a key enabler for a wide range of mobile computing applications. Fingerprint-based localization using channel state information (CSI) has attracted significant attention due to its high accuracy and compatibility with existing communication infrastructures. However, traditional similarity-based fingerprinting methods suffer from high computational complexity and limited scalability in high-dimensional CSI spaces, while purely learning-based approaches fail to explicitly exploit correlations among reference fingerprints during inference. To address these challenges, this paper proposes a unified retrieval-assisted fingerprinting localization framework that tightly integrates similarity-based and learning-based paradigms. Specifically, channel charting is employed to project high-dimensional CSI into a low-dimensional latent space, enabling…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization · Millimeter-Wave Propagation and Modeling
