This paper proposes a novel scheme to enhance the accuracy of packet-switched network synchronization systems by estimating path asymmetry (PA) using convolutional denoising autoencoders (CDAEs). Network synchronization is a key enabler of several emerging applications, with increasingly tight accuracy requirements especially for 5G. Path asymmetry, which arises due to physical and stochastic network conditions, severely degrades synchronization accuracy. In this paper, we propose a novel technique based on the IEEE Precision Time Protocol (PTP), which accurately reconstructs PA information from PTP packets. The proposed PA estimator can be integrated with existing synchronization systems as a pre-processing method to enhance the overall performance. Simulation results using industry-standard traffic profiles demonstrate significant improvements in PA estimation accuracy compared to the state of the art.

Path Asymmetry Reconstruction via Deep Learning / N. Alhashmi, N. Almoosa, G. Gianini (IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE). - In: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)[s.l] : IEEE, 2022. - ISBN 978-1-6654-4280-0. - pp. 1171-1176 (( Intervento presentato al 21. convegno IEEE Mediterranean Electrotechnical Conference (MELECON) tenutosi a Palermo nel 2022 [10.1109/MELECON53508.2022.9842892].

Path Asymmetry Reconstruction via Deep Learning

G. Gianini
Ultimo
2022

Abstract

This paper proposes a novel scheme to enhance the accuracy of packet-switched network synchronization systems by estimating path asymmetry (PA) using convolutional denoising autoencoders (CDAEs). Network synchronization is a key enabler of several emerging applications, with increasingly tight accuracy requirements especially for 5G. Path asymmetry, which arises due to physical and stochastic network conditions, severely degrades synchronization accuracy. In this paper, we propose a novel technique based on the IEEE Precision Time Protocol (PTP), which accurately reconstructs PA information from PTP packets. The proposed PA estimator can be integrated with existing synchronization systems as a pre-processing method to enhance the overall performance. Simulation results using industry-standard traffic profiles demonstrate significant improvements in PA estimation accuracy compared to the state of the art.
Deep learning; Path Asymmetry
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
IEEE
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
2022 - Path_Asymmetry_Reconstruction_via_Deep_Learning.pdf

accesso aperto

Tipologia: Pre-print (manoscritto inviato all'editore)
Dimensione 7.33 MB
Formato Adobe PDF
7.33 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/936355
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact