Self-Supervised Learning for Wireless Localization
<p id="p1">In this chapter, we provide an overview of several data-driven techniques for wireless localization. We initially discuss shallow dimensionality reduction (DR) approaches and investigate a supervised learning method. Subsequently, we transition into deep metric learning and then place par...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89332 |
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| Sumario: | <p id="p1">In this chapter, we provide an overview of several data-driven techniques for wireless localization. We initially discuss shallow dimensionality reduction (DR) approaches and investigate a supervised learning method. Subsequently, we transition into deep metric learning and then place particular emphasis on a transformer-based model and self-supervised learning. We highlight a new research direction of employing designed pretext tasks to train AI models, enabling them to learn compressed channel features useful for wireless localization. We use datasets obtained in massive multiple-input multiple-output (MIMO) systems indoors and outdoors to investigate the performance of the discussed approaches.</p> |
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| ISBN: | 978-1-80356-167-7 |
| DOI: | 10.5772/intechopen.1003773 |