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.

Alhashmi, N., Almoosa, N., Gianini, G. (2022). Path Asymmetry Reconstruction via Deep Learning. In MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings (pp.1171-1176). IEEE [10.1109/MELECON53508.2022.9842892].

Path Asymmetry Reconstruction via Deep Learning

Gianini, G
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.
paper
Deep Learning; IEEE 1588 Precision Time Protocol; Machine Learning; Path Asymmetry; PTP; Time Synchronization;
English
21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022 - 14 June 2022 through 16 June 2022
2022
MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings
9781665442800
2022
1171
1176
reserved
Alhashmi, N., Almoosa, N., Gianini, G. (2022). Path Asymmetry Reconstruction via Deep Learning. In MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings (pp.1171-1176). IEEE [10.1109/MELECON53508.2022.9842892].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454822
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