IDEAS home Printed from https://ideas.repec.org/a/wly/jnlmpe/v2023y2023i1n1695589.html

A Novel Switching Algorithm to the Preferred Clock Skew Estimator Applicable for the PTP Case in the Fractional Gaussian Noise Environment

Author

Listed:
  • Yehonatan Avraham
  • Monika Pinchas

Abstract

Recently, the same authors provided a switching algorithm for the preferred clock skew estimator appropriate for the precision time protocol (PTP) scenario. The algorithm chooses the one‐way delay (OWD) clock skew estimator for the Forward path or the Reverse path or the two‐way delay (TWD) clock skew estimator that has the best performance in the mean square error (MSE) perspective. However, the switching algorithm applies only to the Gaussian scenario. In a real system, the packet delay variation (PDV) can be characterized as an fractional Gaussian noise (fGn) process where the Hurst exponent parameter can also have values higher than 0.5. Thus, the Gaussian case‐switching algorithm may not apply in a real system where after a small set of PTP measurements, the switching algorithm should be able to switch effectively to the preferred clock skew estimator with the best performance in the MSE perspective. In this paper, the PDV is characterized as an fGn process where the Hurst exponent (H) is in the range of 0.5 ≤ H

Suggested Citation

  • Yehonatan Avraham & Monika Pinchas, 2023. "A Novel Switching Algorithm to the Preferred Clock Skew Estimator Applicable for the PTP Case in the Fractional Gaussian Noise Environment," Mathematical Problems in Engineering, John Wiley & Sons, vol. 2023(1).
  • Handle: RePEc:wly:jnlmpe:v:2023:y:2023:i:1:n:1695589
    DOI: 10.1155/2023/1695589
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2023/1695589
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2023/1695589?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. Li, Ming, 2021. "Generalized fractional Gaussian noise and its application to traffic modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    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.

      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:wly:jnlmpe:v:2023:y:2023:i:1:n:1695589. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/2629 .

      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.