Self-Supervised Learning for User Localization

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

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 646 × 943 × 41 meters, and our approach demonstrates promising results even for such expansive localization tasks.

Identifier

85197907423 (Scopus)

ISBN

[9798350370997]

Publication Title

2024 International Conference on Computing, Networking and Communications, ICNC 2024

External Full Text Location

https://doi.org/10.1109/ICNC59896.2024.10555943

First Page

886

Last Page

890

Grant

693JJ320C000021

Fund Ref

Federal Highway Administration

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