2020/02/28 by M. Goodarzi, Meysam Goodarzi, Goodarzi, M. +12 · 1 citation
Computer Science · Engineering · Mathematics · #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Network Time Synchronization Technologies #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #Wireless Body Area Networks #cs.LG #cs.NI #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.12660
arxiv created 2020/02/28 · openalex publication_date 2020/02/28 · arxiv updated 2020/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.