Approximating Optimal Estimation of Time Offset Synchronization with Temperature Variations
This addresses clock synchronization issues in protocols affected by temperature variations, but it is incremental as it builds on existing estimation methods.
The paper tackled the problem of time offset synchronization under temperature variations, which create a non-Gaussian environment, by developing a functional optimization approach with neural network training and spline regression heuristics, resulting in improved performance over suboptimal Kalman filtering.
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.