Self-Supervised Learning

In this chapter you can find an overview of several data-driven techniques for feature learning of radio-frequency (RF) signals. From shallow dimensionality reduction to deep metric learning and self-supervised learning, we discuss different approaches to leveraging channel estimates for wireless localization.

We propose a self-supervised method that learns general-purpose channel features from unlabeled data without relying on contrastive CSI estimates. Furthermore, we investigate varying Transformer attention settings to leverage antenna and subcarrier diversity.

Whether contrastive triplet-loss is better than classical cross-entropy based classifier for low-dimensional CSI representation learning.

A Simple ChatGPT Demo