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Complex-Valued Networks for Automatic Modulation Classification
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DOI：10.1109/TVT.2020.3005707.png)
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Abstract
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Deep learning (DL) has been recognized as an effective solution for automatic modulation classification (AMC). However, most recent DL based AMC works are based on real-valued operations and representations. In this correspondence, we aim to demonstrate the high potential of complex-valued networks for AMC. We present the design of several key building blocks for complex-valued networks, such as complex convolution, complex batch-normalization, complex weight initialization, and complex dense strategies. We then provide a comparison study of three different neural network models and their complex-valued counterparts using the RadioML 2016.10 A dataset. Our results validate the superior performance in AMC achieved by the complex-valued networks.
Keywords:
Convolution
Neural networks
Modulation
Kernel
Wireless communication
Computer architecture
Signal to noise ratio
Automatic modulation classification
deep learning
complex-valued networks
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