Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
This addresses the challenge of spectrum awareness in wireless communications, particularly for automatic modulation recognition, by reducing the need for labeled data, which can save time and costs.
The paper tackles the problem of limited labeled data for deep learning in wireless signal classification by proposing a self-supervised learning method that learns representations from RF signals, resulting in an almost order-of-magnitude increase in sample efficiency and higher accuracy compared to state-of-the-art methods.
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.