Automatic Modulation Classification via Meta-Learning

• Published in: IEEE internet of things journal Vol. 10; no. 14; pp. 12276 - 12292
• Main Authors: Hao, Xiaoyang, Feng, Zhixi, Yang, Shuyuan, Wang, Min, Jiao, Licheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automatic modulation classification (AMC); Bias; Classification; Convolutional neural networks; distribution bias; Feature extraction; few-shot; Frequency modulation; Internet of Things; Machine learning; meta-learning; Modulation; multi-frequency octave resnet (MFOR); Signal classification; Signal distribution; Task analysis; Training
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3247162

Internet of Things (IoT) networks are often subject to many malicious attacks in untrusted environments, and automatic modulation classification (AMC) is an effective way to combat IoT physical-layer threats. However, most existing AMC methods assume sufficient labeled signals and invariant signal distribution, which is often impossible in untrusted environments. In this article, a new meta-learning method is proposed for a few-shot AMC with distribution bias. First, a multi-frequency octave ResNet (MFOR) is constructed to learn coarse (low-frequency) and fine (high-frequency) features, which can efficiently identify the modulation type of the signal while saving computational resources. Second, a large number of classification-related meta-tasks are established for training MFOR to explore general knowledge in signal classification, and then transfer it to the AMC. Different with deep neural networks (DNNs) that learn a mapping by multiple instances, the MFOR with meta-learning (denoted as M-MFOR) can improve the generalization ability of new AMC tasks with very few instances and distribution bias. Furthermore, we find that the distribution bias between data can be reduced by adjusting the normalized distribution and propose a class-related mixup. Extensive experiments are taken on several datasets to investigate the effectiveness of M-MFOR. The results show its feasibility and superiority over existing methods.