On the Optimum Design of L-Estimators for Phase Offset Estimation in IEEE 1588
On the Optimum Design of L-Estimators for Phase Offset Estimation in IEEE 1588
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DOI：
10.1109/tcomm.2015.2493534
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发表时间：
2015-10
影响因子：
8.3
通讯作者：
Anand Guruswamy;Rick S. Blum;S. Kishore;Mark Bordogna
中科院分区：
文献类型：
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作者：
Anand Guruswamy;Rick S. Blum;S. Kishore;Mark Bordogna
在诸如IEEE 1588的基于分组的时间同步协议中，经由主设备和从设备之间的双向消息交换来确定时钟相位偏移。由于分组网络中的端到端延迟本质上是随机的，因此从消息交换中恢复相位偏移必须被视为统计估计问题。最近，作者提出了这个问题的极大极小估计，这是最佳的最小化的均方估计误差的所有值的未知参数。在本文中，我们考虑一类被称为L-估计，这是顺序统计量的线性函数的限制估计。本文研究了在几个迄今未考虑的最优性准则下的最优L-估计量的设计问题。我们的研究结果解决的情况下，排队延迟分布是完全已知的，以及网络模型的不确定性存在的情况下。最佳L-估计，利用信息从过去的观察窗口，以提高性能。导出的L-估计有一个更低的计算复杂度比极小极大估计，也需要较少的排队延迟的统计知识。仿真结果表明，L-估计表现出的均方估计误差非常接近minimax估计在许多网络场景。
In packet-based time synchronization protocols such as IEEE 1588, clock phase offsets are determined via two-way message exchanges between a master and a slave. Since the end-to-end delays in packet networks are inherently stochastic in nature, the recovery of phase offsets from message exchanges must be treated as a statistical estimation problem. Recently, minimax estimators for this problem were proposed by the authors, which are optimum in terms of minimizing the mean-squared estimation error over all values of the unknown parameters. In this paper, we consider a restricted class of estimators referred to as L-estimators, which are linear functions of order statistics. The problem of designing optimum L-estimators is studied under several hitherto unconsidered criteria of optimality. Our results address the case where the queuing delay distributions are fully known, as well as the case where network model uncertainty exists. Optimum L-estimators that utilize information from past observation windows to improve performance are also described. The derived L-estimators have a much lower computational complexity than minimax estimators, and also require lesser statistical knowledge of the queuing delays. Simulation results indicate that L-estimators exhibit a mean-squared estimation error very close to minimax estimators under many network scenarios.