# Outdoor Position Recovery from HeterogeneousTelco Cellular Data

arXiv: https://arxiv.org/abs/2108.10613 · HTML: https://mlsanity.com/paper/2108.10613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System](https://mlsanity.com/paper/2106.13632/) (2021)
- [Explaining the Power-law Distribution of Human Mobility Through Transportation Modality Decomposition](https://mlsanity.com/paper/1408.4910/) (2014)
- [Stride Length Estimation with Deep Learning](https://mlsanity.com/paper/1609.03321/) (2016)
- [The Diversity and Scale Matter: Ubiquitous Transportation Mode Detection using Single Cell Tower Information](https://mlsanity.com/paper/1502.01454/) (2015)

## Abstract (arXiv)

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. we propose a multi-task learning-based deep neural network (DNN) framework, namely PRNet+, to incorporate outdoor position recovery and transportation mode detection. To make sure the framework work, PRNet+ develops a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that PRNet+ greatly outperforms state-of-the-arts.