Self-Supervised RF Signal Representation Learning for NextG Signal Classification With Deep Learning

Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wireless signals. Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available. We present a self-supervised RF signal representation learning method and apply it to the automatic modulation recognition (AMR) task by specifically formulating a set of transformations to capture the wireless signal characteristics. We show that the sample efficiency (the number of labeled samples needed to achieve a certain performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with SSL. This translates to substantial time and cost savings. Furthermore, SSL increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy when limited training data is available.

Automated summary

Deep learning has wide applications in the wireless domain for improving spectrum awareness. We propose a self-supervised RF signal representation learning method and apply it to automatic modulation recognition task. By capturing the wireless signal characteristics through a set of transformations, we demonstrate that self-supervised learning significantly increases the sample efficiency of automatic modulation recognition and maintains high accuracy even with limited training data.