# Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images

arXiv: https://arxiv.org/abs/2401.05711 · HTML: https://mlsanity.com/paper/2401.05711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [A Tutorial on Near-Field XL-MIMO Communications Towards 6G](https://mlsanity.com/paper/2310.11044/) (2023)
- [Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning](https://mlsanity.com/paper/2101.10527/) (2021)
- [Carrier Phase Ranging for Indoor Positioning with 5G NR Signals](https://mlsanity.com/paper/2112.11772/) (2021)
- [AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information](https://mlsanity.com/paper/1912.10108/) (2019)

## Built on by (most cited first, 1 of 1)

- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)

## Abstract (arXiv)

While fingerprinting localization is favored for its effectiveness, it is hindered by high data acquisition costs and the inaccuracy of static database-based estimates. Addressing these issues, this letter presents an innovative indoor localization method using a data-efficient meta-learning algorithm. This approach, grounded in the ``Learning to Learn'' paradigm of meta-learning, utilizes historical localization tasks to improve adaptability and learning efficiency in dynamic indoor environments. We introduce a task-weighted loss to enhance knowledge transfer within this framework. Our comprehensive experiments confirm the method's robustness and superiority over current benchmarks, achieving a notable 23.13% average gain in Mean Euclidean Distance, particularly effective in scenarios with limited CSI data.