论文标题
在出现意外确定性路径延迟不对称的情况下
Robust Clock Skew and Offset Estimation for IEEE 1588 in the Presence of Unexpected Deterministic Path Delay Asymmetries
论文作者
论文摘要
IEEE 1588建立在经典的双向消息交换方案上,是针对数据包切换网络的流行时钟同步协议。由于数据包切换网络中存在随机排队延迟,因此时钟偏斜的联合恢复并偏移了交换同步数据包的时间戳,可以将其视为统计估计问题。在本文中,我们解决了IEEE 1588的时钟偏斜问题和偏移估计的问题,在存在{\ color {\ color {black {black {black {black {black {black {black {black {black {black {black {black {black {black}确定性路径延迟}之间,从而可能由不正确的模型或网络攻击而导致反向奴隶路径和反向slave-to-master路径。首先,假设有多个主奴隶通信路径和概率密度函数(PDF)的完整知识,我们在IEEE 1588的时钟偏斜和IEEE 1588的偏移估计方案上开发了下限。近似于高斯随机变量的混合物近似随机排队延迟的PDF,然后我们提出了强大的迭代时钟偏斜和偏移估计方案,该方案采用了空间交替的广义期望 - 最大化(SAGE)算法来学习所有未知参数。数值结果表明,开发的鲁棒方案表现出接近下限的均方根估计误差。
IEEE 1588, built on the classical two-way message exchange scheme, is a popular clock synchronization protocol for packet-switched networks. Due to the presence of random queuing delays in a packet-switched network, the joint recovery of the clock skew and offset from the timestamps of the exchanged synchronization packets can be treated as a statistical estimation problem. In this paper, we address the problem of clock skew and offset estimation for IEEE 1588 in the presence of possible unknown asymmetries between the {\color{black} deterministic path delays} of the forward master-to-slave path and reverse slave-to-master path, which can result from incorrect modeling or cyber-attacks. First, we develop lower bounds on the mean square estimation error for a clock skew and offset estimation scheme for IEEE 1588 assuming the availability of multiple master-slave communication paths and complete knowledge of the probability density functions (pdf) describing the random queuing delays. Approximating the pdf of the random queuing delays by a mixture of Gaussian random variables, we then present a robust iterative clock skew and offset estimation scheme that employs the space alternating generalized expectation-maximization (SAGE) algorithm for learning all the unknown parameters. Numerical results indicate that the developed robust scheme exhibits a mean square estimation error close to the lower bounds.