Rate-constrained traffic prediction of TTE network based on LSTM

被引：0

作者：

Shi Y. ^{[1
]}

Li Q. ^{[1
]}

Xiong H. ^{[1
]}

机构：

[1] School of Electronic and Information Engineering, Beihang University, Beijing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 04期

关键词：

Long short-term memory (LSTM) algorithm; Network simulation; Rate-constrained (RC) traffic; Time-triggered Ethernet (TTE) network; Traffic prediction;

D O I：

10.13700/j.bh.1001-5965.2019.0320

中图分类号：

学科分类号：

摘要：

The rate constraint (RC) traffic in time triggered Ethernet (TTE) is event-triggered traffic. In the application scenario of dynamic scheduling of RC traffic, if it can predict the sequence of several RC traffic arriving at the switching node in a short time in the future, the switching node can make scheduling decision in advance to reduce RC traffic delay and improve network throughput. In this paper, the arrival sequence model of RC traffic is established, and an algorithm of RC traffic prediction based on long-term memory network (LSTM) is proposed. Using OMNET++ tool to simulate TTE network, we can get the data of RC traffic transmission under multiple groups of mixed critical configuration, and train and test the prediction algorithm as an input sample. The experimental results show that the accuracy of LSTM algorithm in RC traffic prediction is more than 70%. The experimental results show that the proposed algorithm is suitable for RC traffic prediction scenarios. © 2020, Editorial Board of JBUAA. All right reserved.

引用

页码：822 / 829

页数：7

共 22 条

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