Refined Semi-Supervised Modulation Classification: Integrating Consistency Regularization and Pseudo-Labeling Techniques

Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi ()
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Min Ma: School of Network and Communication, Nanjing Vocational College of Information Technology, Nanjing 210023, China
Shanrong Liu: Nanjing Branch, Beijing Xiaomi Mobile Software Co., Ltd., Nanjing 210026, China
Shufei Wang: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Shengnan Shi: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Future Internet, 2024, vol. 16, issue 2, 1-14

Abstract: Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processing without prior information. While deep learning has been applied to AMC, its effectiveness largely depends on the availability of labeled samples. To address the scarcity of labeled data, we introduce a novel semi-supervised AMC approach combining consistency regularization and pseudo-labeling. This method capitalizes on the inherent data distribution of unlabeled data to supplement the limited labeled data. Our approach involves a dual-component objective function for model training: one part focuses on the loss from labeled data, while the other addresses the regularized loss for unlabeled data, enhanced through two distinct levels of data augmentation. These combined losses concurrently refine the model parameters. Our method demonstrates superior performance over established benchmark algorithms, such as decision trees (DTs), support vector machines (SVMs), pi-models, and virtual adversarial training (VAT). It exhibits a marked improvement in the recognition accuracy, particularly when the proportion of labeled samples is as low as 1–4%.

Keywords: automatic modulation classification; semi-supervised learning; consistency regularization; data augmentation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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