# Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios

arXiv: https://arxiv.org/abs/2511.14076 · HTML: https://mlsanity.com/paper/2511.14076/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning](https://mlsanity.com/paper/1906.08171/) (2019)
- [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems](https://mlsanity.com/paper/1804.05347/) (2018)
- [Graph Neural Network for Large-Scale Network Localization](https://mlsanity.com/paper/2010.11653/) (2020)
- [Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform](https://mlsanity.com/paper/2010.10233/) (2020)
- [A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings](https://mlsanity.com/paper/2012.06755/) (2020)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization](https://mlsanity.com/paper/1910.06895/) (2019)
- [CSI-Based Localization with CNNs Exploiting Phase Information](https://mlsanity.com/paper/2101.08983/) (2021)
- [Optimal preprocessing of WiFi CSI for sensing applications](https://mlsanity.com/paper/2307.12126/) (2023)
- [A Meta-learning based Generalizable Indoor Localization Model using Channel State Information](https://mlsanity.com/paper/2305.13453/) (2023)
- [Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization](https://mlsanity.com/paper/2311.16856/) (2023)
- [A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization](https://mlsanity.com/paper/2005.06394/) (2020)
- [Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data](https://mlsanity.com/paper/2405.06299/) (2024)
- [AI Generated Signal for Wireless Sensing](https://mlsanity.com/paper/2312.14563/) (2023)
- [A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks](https://mlsanity.com/paper/2205.08068/) (2022)
- [Enhancing Feature Extraction for Indoor Fingerprint Localization Using Diversified Data](https://mlsanity.com/paper/2306.17634/) (2023)
- [Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images](https://mlsanity.com/paper/2401.05711/) (2024)

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

- [Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks](https://mlsanity.com/paper/2606.20583/) (2026)
- [PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization](https://mlsanity.com/paper/2609.14469/) (2026)

## Abstract (arXiv)

To promote the practicality of deep learning-based localization, existing studies aim to address the issue of scenario dependence through meta-learning. However, these studies primarily focus on variations in environmental layouts while overlooking the impact of changes in device configurations, such as bandwidth, the number of access points (APs), and the number of antennas used. Unlike environmental changes, variations in device configurations affect the dimensionality of channel state information (CSI), thereby compromising neural network usability. To address this issue, we propose Meta-SimGNN, a novel WiFi localization system that integrates graph neural networks with meta-learning to improve localization generalization and robustness. First, we introduce a fine-grained CSI graph construction scheme, where each AP is treated as a graph node, allowing for adaptability to changes in the number of APs. To structure the features of each node, we propose an amplitude-phase fusion method and a feature extraction method. The former utilizes both amplitude and phase to construct CSI images, enhancing data reliability, while the latter extracts dimension-consistent features to address variations in bandwidth and the number of antennas. Second, a similarity-guided meta-learning strategy is developed to enhance adaptability in diverse scenarios. The initial model parameters for the fine-tuning stage are determined by comparing the similarity between the new scenario and historical scenarios, facilitating rapid adaptation of the model to the new localization scenario. Extensive experimental results over commodity WiFi devices in different scenarios show that Meta-SimGNN outperforms the baseline methods in terms of localization generalization and accuracy.