Offset Estimation Based on ARIMA-LSTM for Time Synchronization in Single Twisted Pair Ethernet

Guanwen Cui, Zhezhuang Xu, Xuchao Gao, Songbing Lin, Yi Guo
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Abstract

Single twisted pair Ethernet becomes popular in the industrial internet of thing (IIoT), since it can use only one twisted pair to provide high speed data transmission while the cables of the field bus can be reused. However, since its transmission medium is inferior to traditional Ethernet, it is easier to generate delay jitter that greatly impacts the accuracy of time synchronization. To solve this problem, in this paper, an offset estimation method based on AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) is proposed to estimate the clock offset when the delay jitter appears. The offset estimation model is firstly obtained by training the ARIMA-LSTM with offline offset data. When the delay jitter is detected, the offset can be estimated by the model to replace the unreliable offset obtained by the time synchronization protocol. Experiments are executed in the testbed, and the results prove that the proposed method can improve the time synchronization accuracy in the single twisted pair Ethernet.
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基于ARIMA-LSTM的双绞线以太网时间同步偏移估计
单双绞线以太网在工业物联网(IIoT)中很受欢迎,因为它只需要一个双绞线就可以提供高速数据传输,同时现场总线的电缆可以重复使用。但由于其传输介质不如传统的以太网,因此更容易产生时延抖动,极大地影响了时间同步的准确性。为了解决这一问题,本文提出了一种基于自回归综合移动平均(ARIMA)和长短期记忆(LSTM)的时钟偏移估计方法,用于估计出现延迟抖动时的时钟偏移。首先利用离线偏移量数据对ARIMA-LSTM进行训练,得到偏移量估计模型;当检测到延迟抖动时,该模型可以估计出偏移量,以取代时间同步协议获得的不可靠偏移量。在实验台上进行了实验,结果证明该方法可以提高单双绞线以太网的时间同步精度。
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