Self-Supervised Learning for Wireless Localization Article Swipe

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Computer science Artificial intelligence Wireless Machine learning Supervised learning Deep learning Metric (unit) MIMO Channel (broadcasting) Engineering Telecommunications Artificial neural network Operations management

Artan Salihu , Markus Rupp , Štefan Schwarz ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.5772/intechopen.1003773 · OA: W4389194270

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.