Attention

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.

I attended the EEML 2022 and presented the work on wireless transformer (WiT).

I presented the WiT at SPAWC 2022.

We show that the whole estimated channel can be fed into the transformer block as a set of subcarriers. Without vectorizing the input, using recurrence, standard convolution operators, or fusion, attention can serve as an adaptive filter for resilient CSI.