A Retrieval-Assisted Framework for Wireless Localization

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 efficient and scalable retrieval of locally correlated reference points (RPs). Building upon the retrieved RPs, a graph attention network (GAT) is designed to explicitly model inter-sample correlations between the query CSI and its associated references, allowing adaptive and geometry-aware feature aggregation for accurate position estimation. Extensive experiments conducted on both real-world indoor and ray-tracing simulated outdoor scenarios demonstrate that the proposed method consistently outperforms state-of-the-art similarity-based and learning-based localization approaches.