IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0234068.html
   My bibliography  Save this article

Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks

Author

Listed:
  • Hui Li
  • Ming Li

Abstract

This research aims to analyze the effects of different parameter estimation on the recognition performance of satellite modulation signals based on deep learning (DL) under low signal to noise ratio (SNR) or channel non-ideal conditions. In this study, first, the common characteristics of broadband satellite modulation signal and the commonly used signal feature extraction algorithm are introduced. Then, the broadband satellite modulation signal pattern recognition model based on deformable convolutional neural networks (DCNN) is built, and the broadband satellite signal simulation is conducted based on Matlab software. Next, the signal characteristics of binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 8 phase shift keying (PSK), 16 quadratic amplitude modulation (QAM), 64QAM, and 32 absolute phase shift keying (APSK) are extracted by the constellation map, and the ratio changes of T1 and T2 with SNR are compared. When SNR is given, it is compared with VGG model, AlexNet model, and ResNe model. The results show that the constellation points of satellite signals with different modulations are evenly distributed. T1 of PSK modulation signals increases significantly with the increase of SNR. When SNR is greater than 10, PSK modulation signals can be identified. When T2 is set and SNR is greater than 15dB, 16QAM and 32APSK signals can be distinguished. In the model, the Relu activation function, mini-batch gradient descent (MBGD) algorithm, and Softmax classifier have the best recognition accuracy. PSK modulation signals have the best recognition rate when the SNR is 0dB, and the recognition accuracy of different modulation signals at 20dB is over 98%. When the data length reaches 4000, the recognition accuracy of different modulation signals is higher than 97%. Compared with other algorithms, this algorithm has the highest recognition accuracy (99.83%) and shorter training time (3960s). In conclusion, the broadband satellite modulation signal pattern recognition algorithm of DCNN constructed in this study can effectively identify the patterns of different modulation signals.

Suggested Citation

  • Hui Li & Ming Li, 2020. "Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-14, July.
    • Handle: RePEc:plo:pone00:0234068
    DOI: 10.1371/journal.pone.0234068
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234068
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0234068&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0234068?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Modisane B. Seitshiro & Hopolang P. Mashele & Stephanos Papadamou, 2020. "Assessment of model risk due to the use of an inappropriate parameter estimator," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1710970-171, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0234068. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.