# Mobility-Induced Graph Learning for WiFi Positioning

arXiv: https://arxiv.org/abs/2311.08271 · HTML: https://mlsanity.com/paper/2311.08271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [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)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Graph Neural Network for Large-Scale Network Localization](https://mlsanity.com/paper/2010.11653/) (2020)
- [Learning Cross-Domain Representation with Multi-Graph Neural Network](https://mlsanity.com/paper/1905.10095/) (2019)
- [A Novel GCN based Indoor Localization System with Multiple Access Points](https://mlsanity.com/paper/2104.10342/) (2021)
- [Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach](https://mlsanity.com/paper/2011.03698/) (2020)
- [Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS](https://mlsanity.com/paper/2104.11316/) (2021)
- [Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach](https://mlsanity.com/paper/2112.14138/) (2021)
- [Traffic Convexity Aware Cellular Networks: A Vehicular Heavy User Perspective](https://mlsanity.com/paper/1501.02594/) (2015)
- [Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning](https://mlsanity.com/paper/2105.07475/) (2021)

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

- [Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization](https://mlsanity.com/paper/2311.16856/) (2023)

## Abstract (arXiv)

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user's relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user's stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE's effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say root mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and semi-supervised learning cases, respectively.