IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v85y2024i3d10.1007_s11235-023-01092-4.html
   My bibliography  Save this article

Blind recognition algorithm of modulation mode and space–time block code via convolutional neural network

Author

Listed:
  • Pan Deng

    (Chongqing University of Posts and Telecommunications
    Yibin University)

  • Tianqi Zhang

    (Chongqing University of Posts and Telecommunications)

  • Lianghua Wen

    (Yibin University)

  • Baoze Ma

    (Chongqing University of Posts and Telecommunications)

  • Debang Liu

    (Chongqing University of Posts and Telecommunications)

Abstract

Concentrating on the joint recognition problem of modulation and space–time coding, algorithm for blind identification of modulation and space–time coding based upon fourth-order cyclic cumulant and feature parameter extraction in correlation matrix is proposed in this paper. The algorithm first uses the fourth-order cyclic cumulant to identify different modulation modes (4PSK, 8PSK, 16QAM, 32QAM, 64QAM). Different space–time block codes (Alamouti, OSTBC3, OSTBC4, NOSTBC2, and NOSTBC4) are identified by extracting feature parameters in the correlation matrix. The five recognized modulation modes and five space–time codes are sent to convolutional neural network, then 25 combination modes of modulation and space–time coding are recognized by using the Dropout layer, Gaussian noise layer, Flatten layer, dense layer, and fully connected layer successively by applying the Softmax activation function. The research results indicate that the emanated algorithm may effectively identify 5 modulation modes and 5 space–time codes, and can identify 25 combination modes of modulation and space–time coding. Under the condition that SNR is greater than or equal to 0 dB, the joint recognition rate of modulation and space–time coding can reach 100%.

Suggested Citation

  • Pan Deng & Tianqi Zhang & Lianghua Wen & Baoze Ma & Debang Liu, 2024. "Blind recognition algorithm of modulation mode and space–time block code via convolutional neural network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 85(3), pages 425-442, March.
    • Handle: RePEc:spr:telsys:v:85:y:2024:i:3:d:10.1007_s11235-023-01092-4
    DOI: 10.1007/s11235-023-01092-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-023-01092-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-023-01092-4?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:telsys:v:85:y:2024:i:3:d:10.1007_s11235-023-01092-4. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.