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.

翻译：深度神经网络在通信信号识别中已被广泛应用并取得了显著性能，但这种优势通常依赖于使用大量样本进行监督学习，而在小规模标注数据集上训练深度神经网络往往会导致过拟合，从而造成性能退化。为此，我们提出了一种半监督学习方法，有效利用大量更易获取的无标注信号数据来提升泛化能力。该方法的核心是一种基于一致性正则化的新实现——交换预测，通过强数据增强扰动无标注样本，并促使模型对该样本的预测结果与原始预测保持一致，采用带缩放交叉熵损失和交换对称性进行优化。大量实验表明，我们提出的方法能够为通信信号识别的深度半监督学习取得令人满意的结果。