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

• URL: http://arxiv.org/abs/2305.13453v2
• Date: Tue, 13 Jun 2023 18:29:25 GMT
• Title: A Meta-learning based Generalizable Indoor Localization Model using Channel State Information
• Authors: Ali Owfi, ChunChih Lin, Linke Guo, Fatemeh Afghah, Jonathan Ashdown, Kurt Turck
• Abstract summary: We propose a new meta-learning algorithm, TB-MAML, intended to further improve generalizability when the dataset is limited. We evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learning algorithms.
• Score: 8.302375673936387
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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