Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 69, Issue 9, Pages 10085-10089Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.3005707
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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Funding
- National Natural Science Foundation of China [61771154]
- Fundamental Research Funds for the Central Universities [3072020CF0813]
- Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities [3072020GIP0813]
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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.
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