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

TL;DR

This paper introduces wireless channel-aware data augmentation techniques for deep learning-based indoor localization, leveraging domain knowledge of wireless channels to improve accuracy especially in low-data scenarios.

Contribution

It proposes novel data augmentation methods based on wireless propagation channel characteristics, enhancing indoor localization accuracy with limited training data.

Findings

Localization accuracy improves up to 50% with augmentation in low-data regimes.

Proposed methods outperform measurement-only approaches by up to 33% with less data.

Effective even when combined with Transfer Learning for data scarcity issues.

Abstract

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.