Path Asymmetry Reconstruction via Deep Learning

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.