IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i3id192.html

Modulation Classifier

Author

Listed:
  • J Roopesh
  • Amrutha DC
  • Meghana Bhushan S N
  • Keerti Kulkarni

Abstract

In the realm of wireless communications, automatic modulation classification (AMC) plays a crucial role in identifying the modulation type of incoming signals, enabling efficient spectrum usage in congested wireless environments and other communication systems applications. We present an AMC that takes into consideration various parameters among them the 3 major parameters are amplitude, power spectral density (PSD), and Signal to noise ratio (SNR), with the help of machine learning that is Support Vector machine in specific, within the MATLAB environment helps us in classifying the different types of signal that is QPSK, BPSK, QAM, 64QAM and PAM4.Therefore our AMC significantly contributes to the wireless communication by significantly boosting the different modulation techniques and thereby improving the spectrum efficiency.

Suggested Citation

  • J Roopesh & Amrutha DC & Meghana Bhushan S N & Keerti Kulkarni, 2024. "Modulation Classifier," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 199-203, June.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:192
    DOI: 10.32628/CSEIT2410319
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410319
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT2410319
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT2410319/CSEIT2410319
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT2410319?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    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:jbh:ijsrcs:v10:y2024:i3:id:192. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.