# Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization

arXiv: https://arxiv.org/abs/2408.06452 · HTML: https://mlsanity.com/paper/2408.06452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning](https://mlsanity.com/paper/1810.04126/) (2018)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning](https://mlsanity.com/paper/1906.08171/) (2019)
- [Self-Supervised and Invariant Representations for Wireless Localization](https://mlsanity.com/paper/2302.07000/) (2023)
- [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems](https://mlsanity.com/paper/1804.05347/) (2018)
- [CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network](https://mlsanity.com/paper/2205.05775/) (2022)
- [Simple and Effective Augmentation Methods for CSI Based Indoor Localization](https://mlsanity.com/paper/2211.10790/) (2022)
- [DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization](https://mlsanity.com/paper/2101.10833/) (2021)
- [Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems](https://mlsanity.com/paper/2211.08291/) (2022)

## Abstract (arXiv)

Indoor localization is a challenging problem that - unlike outdoor localization - lacks a universal and robust solution. Machine Learning (ML), particularly Deep Learning (DL), methods have been investigated as a promising approach. Although such methods bring remarkable localization accuracy, they heavily depend on the training data collected from the environment. The data collection is usually a laborious and time-consuming task, but Data Augmentation (DA) can be used to alleviate this issue. In this paper, different from previously used DA, we propose methods that utilize the domain knowledge about wireless propagation channels and devices. The methods exploit the typical hardware component drift in the transceivers and/or the statistical behavior of the channel, in combination with the measured Power Delay Profile (PDP). We comprehensively evaluate the proposed methods to demonstrate their effectiveness. This investigation mainly focuses on the impact of factors such as the number of measurements, augmentation proportion, and the environment of interest impact the effectiveness of the different DA methods. We show that in the low-data regime (few actual measurements available), localization accuracy increases up to 50%, matching non-augmented results in the high-data regime. In addition, the proposed methods may outperform the measurement-only high-data performance by up to 33% using only 1/4 of the amount of measured data. We also exhibit the effect of different training data distribution and quality on the effectiveness of DA. Finally, we demonstrate the power of the proposed methods when employed along with Transfer Learning (TL) to address the data scarcity in target and/or source environments.