Synchronization in 5G: a Bayesian Approach

TL;DR

This paper introduces a hybrid Bayesian approach combining Kalman Filtering and Factor Graphs with Belief Propagation to improve large-scale network synchronization accuracy, achieving errors below 5 nanoseconds.

Contribution

It presents a novel hybrid synchronization method using Bayesian techniques and domain division for large networks, enhancing precision and scalability.

Findings

Offset estimation error remains below 5 ns

Hybrid approach outperforms traditional methods in accuracy

Domain division improves synchronization efficiency

Abstract

In this work, we propose a hybrid approach to synchronize large scale networks. In particular, we draw on Kalman Filtering (KF) along with time-stamps generated by the Precision Time Protocol (PTP) for pairwise node synchronization. Furthermore, we investigate the merit of Factor Graphs (FGs) along with Belief Propagation (BP) algorithm in achieving high precision end-to-end network synchronization. Finally, we present the idea of dividing the large-scale network into local synchronization domains, for each of which a suitable sync algorithm is utilized. The simulation results indicate that, despite the simplifications in the hybrid approach, the error in the offset estimation remains below 5 ns.