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

• URL: http://arxiv.org/abs/2207.03046v1
• Date: Thu, 7 Jul 2022 02:07:03 GMT
• Title: Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
• Authors: Kemal Davaslioglu, Serdar Boztas, Mehmet Can Ertem, Yalin E. Sagduyu, Ender Ayanoglu
• Abstract summary: Self-supervised learning enables the learning of useful representations from Radio Frequency (RF) signals themselves. We show that the sample efficiency (the number of labeled samples required to achieve a certain accuracy performance) of AMR can be significantly increased by learning signal representations with self-supervised learning.
• Score: 5.624291722263331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, the DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other data domains such as computer vision (in the form of transfer learning) without accounting for the unique characteristics of wireless signals. Self-supervised learning 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 the first self-supervised RF signal representation learning model 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 required to achieve a certain accuracy performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with self-supervised learning. This translates to substantial time and cost savings. Furthermore, self-supervised learning increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy even when a small set of training data samples is used.

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