# Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints

arXiv: https://arxiv.org/abs/2504.04829 · HTML: https://mlsanity.com/paper/2504.04829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Graph Neural Network for Large-Scale Network Localization](https://mlsanity.com/paper/2010.11653/) (2020)
- [CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization](https://mlsanity.com/paper/1910.06895/) (2019)
- [A Novel GCN based Indoor Localization System with Multiple Access Points](https://mlsanity.com/paper/2104.10342/) (2021)
- [Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization](https://mlsanity.com/paper/2311.16856/) (2023)
- [Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization](https://mlsanity.com/paper/2308.14824/) (2023)

## Built on by (most cited first, 2 of 2)

- [RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models](https://mlsanity.com/paper/2606.01899/) (2026)
- [Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning](https://mlsanity.com/paper/2609.09511/) (2026)

## Abstract (arXiv)

Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space. Maintaining high localization accuracy with extremely sparse fingerprints remains a persistent challenge. Existing benchmark methods primarily rely on the measured fingerprints, while neglecting valuable spatial and environmental characteristics. In this paper, we propose a systematic integration of an Attentional Graph Neural Network (AGNN) model, capable of learning spatial adjacency relationships and aggregating information from neighboring fingerprints, and a meta-learning framework that utilizes datasets with similar environmental characteristics to enhance model training. To minimize the labor required for fingerprint collection, we introduce two novel data augmentation strategies: 1) unlabeled fingerprint augmentation using moving platforms, which enables the semi-supervised AGNN model to incorporate information from unlabeled fingerprints, and 2) synthetic labeled fingerprint augmentation through environmental digital twins, which enhances the meta-learning framework through a practical distribution alignment, which can minimize the feature discrepancy between synthetic and real-world fingerprints effectively. By integrating these novel modules, we propose the Attentional Graph Meta-Learning (AGML) model. This novel model combines the strengths of the AGNN model and the meta-learning framework to address the challenges posed by extremely sparse fingerprints. To validate our approach, we collected multiple datasets from both consumer-grade WiFi devices and professional equipment across diverse environments. Extensive experiments conducted on both synthetic and real-world datasets demonstrate that the AGML model-based localization method consistently outperforms all baseline methods using sparse fingerprints across all evaluated metrics.