A Retrieval-Assisted Framework for Wireless Localization

TL;DR

A unified retrieval-assisted fingerprinting localization framework that tightly integrates similarity-based and learning-based paradigms is proposed, demonstrating that the proposed method consistently outperforms state-of-the-art similarity-based and learning-based localization approaches.

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.

Figures (11)

• Figure 1: The proposed retrieval-assisted fingerprinting localization framework, which combines the advantages of similarity-based and learning-based localization.
• Figure 2: Architecture of the proposed CC-GAT-based localization framework, where channel charting is used for efficient RP retrieval and a GAT performs correlation-aware localization.
• Figure 3: GAT-based neural network architecture for localization module.
• Figure 4: A photographic depiction of the environment where the DICHASUS dataset was measuredeuchner2021distributed.
• Figure 5: Top-view layout of the DeepMIMO dataset scenario, highlighting the sampling area and the selected base stationsalkhateeb2019deepmimo.