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http://dx.doi.org/10.7471/ikeee.2022.26.4.568

A Deep Learning-based Automatic Modulation Classification Method on SDR Platforms  

Jung-Ik, Jang (School of Computing, Gachon University)
Jaehyuk, Choi (School of Computing, Gachon University)
Young-Il, Yoon (LIG Nexone)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 568-576 More about this Journal
Abstract
Automatic modulation classification(AMC) is a core technique in Software Defined Radio(SDR) platform that enables smart and flexible spectrum sensing and access in a wide frequency band. In this study, we propose a simple yet accurate deep learning-based method that allows AMC for variable-size radio signals. To this end, we design a classification architecture consisting of two Convolutional Neural Network(CNN)-based models, namely main and small models, which were trained on radio signal datasets with two different signal sizes, respectively. Then, for a received signal input with an arbitrary length, modulation classification is performed by augmenting the input samples using a self-replicating padding technique to fit the input layer size of our model. Experiments using the RadioML 2018.01A dataset demonstrated that the proposed method provides higher accuracy than the existing methods in all signal-to-noise ratio(SNR) domains with less computation overhead.
Keywords
Automatic modulation classification; deep learning; Software-defined radio; SCA; convolution neural network; input size scalable; self-replication padding;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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