Computer Applications and Software, Volume. 42, Issue 4, 263(2025)

MODULATION RECOGNITION BASED ON LIGHTWEIGHT CNN AND ITS APPLICATION ON ARM CORTEX-M EMBEDDED PLATFORM

Dai Taotao, Dian Songyi, and Guo Bin
Author Affiliations
  • College of Electrical Engineering, Sichuan University, Chengdu 610065, Sichuan, China
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    References(12)

    [1] [1] Xu J L, Luo C B, Parr G, et al. A spatiotemporal multichannel learning framework for automatic modulation recognition[J]. IEEE Wireless Communications Letters, 2020, 9 (10): 1629-1632.

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    [3] [3] Pajic M S, Veinovic M, Peric M, et al. Modulation order reduction method for improving the performance of AMC algorithm based on sixth-order cumulants[J]. IEEE Access, 2020, 8: 106386-106394.

    [6] [6] O'Shea T J, Corgan J, Clancy T C. Convolutional radio modulation recognition networks[C]//International Conference on Engineering Applications of Neural Networks, 2016: 213-226.

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    [8] [8] Liu X, Yang D, Gamal A E. Deep neural network architectures for modulation classification[C]//51st Asilomar Conference on Signals, Systems, and Computers, 2017: 915-919.

    [10] [10] Jiang K, Zhang J W, Wu H B, et al. A novel digital modulation recognition algorithm based on deep convolutional neural network[J]. Applied Sciences, 2020, 10(3): 1166.

    [12] [12] Mishkin D, Sergievskiy N, Matas J. Systematic evaluation of convolution neural network advances on the ImageNet[J]. Computer Vision and Image Understanding, 2017, 161: 11-19.

    [13] [13] Ruderman A, Rabinowitz N C, Morcos A S, et al. Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs[EB]. arXiv: 1804.04438, 2018.

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    Dai Taotao, Dian Songyi, Guo Bin. MODULATION RECOGNITION BASED ON LIGHTWEIGHT CNN AND ITS APPLICATION ON ARM CORTEX-M EMBEDDED PLATFORM[J]. Computer Applications and Software, 2025, 42(4): 263

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    Paper Information

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    Received: Oct. 26, 2021

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.3969/j.issn.1000-386x.2025.04.038

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