Achieving Efficient Feature Representation for Modulation Signal: A Cooperative Contrast Learning Approach

Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless networks, and IoT devices inevitably face many malicious active attacks. automatic modulation recognition (AMR) is an effective way to combat IoT physical layer threats. In the field of noncollaborative communicati...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-05, Vol.11 (9), p.16196-16211
Main Authors: Bai, Jing, Wang, Xu, Xiao, Zhu, Zhou, Huaji, Ali, Talal Ahmed Ali, Li, You, Jiao, Licheng
Format: Article
Language:English
Subjects:
Automatic modulation recognition
Clustering
Contrast learning
Feature extraction
feature representation
Hyperplanes
Interference
Internet of Things
Learning
Modulation
modulation recognition
Representations
Self-supervised learning
Signal classification
Signal transmission
Task analysis
Training
unlabeled signal
Wireless networks
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Summary:Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless networks, and IoT devices inevitably face many malicious active attacks. automatic modulation recognition (AMR) is an effective way to combat IoT physical layer threats. In the field of noncollaborative communication, feature representation learning for unlabeled signals is an important task of AMR. However, due to the unavailability of a priori knowledge and the influence of interference during signal transmission, the intercepted unlabeled signals are difficult to perform efficient feature representation. In this article, we propose cooperative contrast learning for unlabeled modulation signal Cooperative Contrast Learning for modulation Signals (CoCL-Sig). Specifically, the CoCL-Sig is trained using both sequence and constellation diagram modalities, and is divided into two parts: 1) modal-level feature representation and 2) instance-level auxiliary feature representation. In modal-level feature representation, two modal projections are matched in the same hyperplane space. To ensure the stability of the feature representation, a sequence auxiliary branch is added to form an instance-level feature representation of the sequence. In addition, the feature representations obtained by the CoCL-Sig can be applied to modulation signals for semi-supervised classification and clustering tasks. We have conducted extensive experiments on two widely used modulation signal data sets, RML2016.10A and RML2016.04C. The results demonstrate the effectiveness of our method in modulation signal feature representation and its superiority compared to other methods.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3350927