Zhu Sanying, Zhou Shutong, Wang Liuquan, Zang Chenxin, Liu Yanqiang, Liu Qiang
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Jiangxi Research Institute, Beihang University, Nanchang 330096, China.
Sensors (Basel). 2024 Oct 10;24(20):6542. doi: 10.3390/s24206542.
With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey-Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition-long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.
随着工业信息化水平的不断提高，海量工业数据需要实时、高保真的无线传输。尽管在过去几十年中已经设计了一些工业无线网络协议，但其中大多数覆盖范围有限且带宽狭窄。它们不能始终确保信息传输的确定性，使得在工业制造领域尤其难以满足低延迟的要求。5G技术具有高传输速率和低延迟的特点；因此，它在工业应用中具有良好的前景。为了将5G技术应用于对数据传输有低延迟要求的工厂环境，在本研究中，我们分析了5G-R15通信系统中往返时间（RTT）的统计性能。结果表明，5G RTT的平均值约为11毫秒，小于WIA-FA的25毫秒。然后，我们将5G RTT数据视为一组时间序列，利用增强迪基-富勒（ADF）检验方法分析RTT数据的稳定性。我们得出结论，RTT数据是非平稳的。因此，首先，对原始的5G RTT序列进行一阶差分，以获得具有更强平稳性的差分序列。然后，提出了一种基于时间序列分析的变分模态分解-长短期记忆（VMD-LSTM）方法来分别预测每个差分序列。最后，对预测结果进行逆差分以获得5G RTT的预测值，4.481%的预测误差表明该方法比LSTM和其他方法表现更好。预测结果可用于根据业务需求评估网络性能，减少指令包丢失的影响，并提高控制算法的鲁棒性。所提出的针对控制问题的预警精度指标还可用于指示何时重新训练模型以及指示控制周期的设置。工业控制领域，特别是在需要低延迟的制造业中，将从该分析中受益。需要注意的是，上述分析和预测方法也适用于R16和R17版本。
