Author:
Salihu Artan,Rupp Markus,Schwarz Stefan
Abstract
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.
Reference53 articles.
1. Rong B. 6G: The next horizon: From connected people and things to connected intelligence. IEEE Wireless Communications. 2021;(5):8-8
2. You X, Wang C-X, Huang J, Gao X, Zhang Z, Wang M, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China Information Sciences. 2021;:1-74
3. Wen F, Wymeersch H, Peng B, Tay WP, So HC, Yang D. A survey on 5G massive MIMO localization. Digital Signal Processing. 2019;:21-28
4. Wymeersch H, Seco-Granados G. Radio localization and sensing—Part ii: State-of-the-art and challenges. IEEE Communications Letters. 2022;(12):2821-2825
5. Wylie M, P, Holtzman J. The non-line of sight problem in mobile location estimation. In: Proceedings of ICUPC-5th International Conference on Universal Personal Communications. Vol. 2. Cambridge, MA, USA: IEEE; 1996. pp. 827-831