Approximating Optimal Estimation of Time Offset Synchronization with Temperature Variations

TL;DR

This paper introduces a neural network-based method for more accurate clock offset synchronization under temperature-induced non-Gaussian conditions, outperforming traditional Kalman filtering.

Contribution

It develops a novel functional optimization approach combined with neural networks and spline regression to improve synchronization accuracy in temperature-variant environments.

Findings

Neural network approximation outperforms Kalman filtering in non-Gaussian settings.

The proposed methods are adaptable to various clock synchronization protocols.

Performance evaluation shows significant accuracy improvements.

Abstract

The paper addresses the problem of time offset synchronization in the presence of temperature variations, which lead to a non-Gaussian environment. In this context, regular Kalman filtering reveals to be suboptimal. A functional optimization approach is developed in order to approximate optimal estimation of the clock offset between master and slave. A numerical approximation is provided to this aim, based on regular neural network training. Other heuristics are provided as well, based on spline regression. An extensive performance evaluation highlights the benefits of the proposed techniques, which can be easily generalized to several clock synchronization protocols and operating environments.