# Self-Supervised Learning for User Localization

arXiv: https://arxiv.org/abs/2404.15370 · HTML: https://mlsanity.com/paper/2404.15370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 19 Apr 2024.

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

- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [On Deep Learning-based Massive MIMO Indoor User Localization](https://mlsanity.com/paper/1804.04826/) (2018)
- [Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network](https://mlsanity.com/paper/2102.03130/) (2021)
- [Reliable Deep Learning based Localization with CSI Fingerprints and Multiple Base Stations](https://mlsanity.com/paper/2111.11839/) (2021)

## Abstract (arXiv)

Machine learning techniques have shown remarkable accuracy in localization tasks, but their dependency on vast amounts of labeled data, particularly Channel State Information (CSI) and corresponding coordinates, remains a bottleneck. Self-supervised learning techniques alleviate the need for labeled data, a potential that remains largely untapped and underexplored in existing research. Addressing this gap, we propose a pioneering approach that leverages self-supervised pretraining on unlabeled data to boost the performance of supervised learning for user localization based on CSI. We introduce two pretraining Auto Encoder (AE) models employing Multi Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) to glean representations from unlabeled data via self-supervised learning. Following this, we utilize the encoder portion of the AE models to extract relevant features from labeled data, and finetune an MLP-based Position Estimation Model to accurately deduce user locations. Our experimentation on the CTW-2020 dataset, which features a substantial volume of unlabeled data but limited labeled samples, demonstrates the viability of our approach. Notably, the dataset covers a vast area spanning over 646x943x41 meters, and our approach demonstrates promising results even for such expansive localization tasks.