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In this work, we propose a hybrid Bayesian approach towards clock offset andskew estimation, thereby synchronizing large scale networks. In particular, wedemonstrate the advantage of Bayesian Recursive Filtering (BRF) in alleviatingtime-stamping errors for pairwise synchronization. Moreover, we indicate thebenefit of Factor Graph (FG), along with Belief Propagation (BP) algorithm inachieving high precision end-to-end network synchronization. Finally, we revealthe merit of hybrid synchronization, where a large-scale network is dividedinto local synchronization domains, for each of which a suitablesynchronization algorithm (BP- or BRF-based) is utilized. The simulationresults show that, despite the simplifications in the hybrid approach, the RootMean Square Errors (RMSEs) of clock offset and skew estimation remain below 5ns and 0.3 ppm, respectively.

A Hybrid Bayesian Approach Towards Clock Offset and Skew Estimation in 5G Networks