  Deep neural networks have been widely used in communication signal
recognition and achieved remarkable performance, but this superiority typically
depends on using massive examples for supervised learning, whereas training a
deep neural network on small datasets with few labels generally falls into
overfitting, resulting in degenerated performance. To this end, we develop a
semi-supervised learning (SSL) method that effectively utilizes a large
collection of more readily available unlabeled signal data to improve
generalization. The proposed method relies largely on a novel implementation of
consistency-based regularization, termed Swapped Prediction, which leverages
strong data augmentation to perturb an unlabeled sample and then encourage its
corresponding model prediction to be close to its original, optimized with a
scaled cross-entropy loss with swapped symmetry. Extensive experiments indicate
that our proposed method can achieve a promising result for deep SSL of
communication signal recognition.
