A Meta-learning based Generalizable Indoor Localization Model using Channel State Information

TL;DR

This paper introduces a meta-learning approach, TB-MAML, to improve the generalizability of indoor localization models based on Channel State Information, enabling better performance in new and dynamic environments.

Contribution

The paper proposes a novel meta-learning algorithm, TB-MAML, specifically designed for indoor localization with limited datasets, enhancing model adaptability across environments.

Findings

TB-MAML outperforms traditional models in new environments.

Meta-learning improves localization accuracy in dynamic settings.

TB-MAML demonstrates robustness with limited training data.

Abstract

Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately estimating the position of wireless devices in indoor environments using wireless parameters such as Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). However, despite the success of deep learning-based approaches in achieving high localization accuracy, these models suffer from a lack of generalizability and can not be readily-deployed to new environments or operate in dynamic environments without retraining. In this paper, we propose meta-learning-based localization models to address the lack of generalizability that persists in conventionally trained DL-based localization models. Furthermore, since meta-learning algorithms require diverse datasets from several different scenarios, which can be hard to collect in the context of localization, we design and propose a new meta-learning algorithm, TB-MAML (Task Biased Model Agnostic Meta Learning), intended to further improve generalizability when the dataset is limited. Lastly, we evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learning algorithms.