论文标题
5G网络中的混合贝叶斯方法的时钟偏移和偏斜估计
A Hybrid Bayesian Approach Towards Clock Offset and Skew Estimation in 5G Networks
论文作者
论文摘要
在这项工作中，我们提出了一种混合贝叶斯的方法来实现时钟偏移和偏斜估计，从而同步了大规模网络。特别是，我们证明了贝叶斯递归滤波（BRF）在减轻成对同步的时戳错误方面的优势。此外，我们指出了因子图（FG）的好处，以及信念传播（BP）算法在实现高精度端到端网络同步方面。最后，我们揭示了混合同步的优点，其中大规模网络被分为局部同步域，用于使用合适的同步算法（基于BP-或BRF）。模拟结果表明，尽管混合方法简化了，但时钟偏移和偏斜估计的根平方误差（RMSE）分别保持在5 ns和0.3 ppm之下。
In this work, we propose a hybrid Bayesian approach towards clock offset and skew estimation, thereby synchronizing large scale networks. In particular, we demonstrate the advantage of Bayesian Recursive Filtering (BRF) in alleviating time-stamping errors for pairwise synchronization. Moreover, we indicate the benefit of Factor Graph (FG), along with Belief Propagation (BP) algorithm in achieving high precision end-to-end network synchronization. Finally, we reveal the merit of hybrid synchronization, where a large-scale network is divided into local synchronization domains, for each of which a suitable synchronization algorithm (BP- or BRF-based) is utilized. The simulation results show that, despite the simplifications in the hybrid approach, the Root Mean Square Errors (RMSEs) of clock offset and skew estimation remain below 5 ns and 0.3 ppm, respectively.
