@inproceedings{6f6a455ceaf840b1829771fca319f471,
title = "Path Asymmetry Reconstruction via Deep Learning",
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.",
keywords = "Deep Learning, IEEE 1588 Precision Time Protocol, Machine Learning, Path Asymmetry, PTP, Time Synchronization",
author = "Nada Alhashmi and Nawaf Almoosa and Gabriele Gianini",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022 ; Conference date: 14-06-2022 Through 16-06-2022",
year = "2022",
doi = "10.1109/MELECON53508.2022.9842892",
language = "British English",
series = "MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1171--1176",
booktitle = "MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings",
address = "United States",
}