A Hierarchical Classification Head Based Convolutional Gated Deep Neural Network for Automatic Modulation Classification

作者：Shuo Chang, Ruiyun Zhang, Kejia Ji, Sai Huang, Zhiyong Feng · 发表于：IEEE Transactions on Wireless Communications · 年份：2022 · DOI：10.1109/twc.2022.3168884 · 被引用次数：54 · 研究领域：Wireless Signal Modulation Classification、Fractal and DNA sequence analysis、Radar Systems and Signal Processing

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse diffe...