无线电工程2024，Vol.54Issue(4)：1034-1042,9.DOI:10.3969/j.issn.1003-3106.2024.04.029
确定性网络5G-A终端时延预测
Deterministic Network 5G-A Terminal Time Delay Prediction
摘要
Abstract
The transmission delay of 5G-A terminals in industrial control scenarios is one of the direct characterizations of deterministic network capabilities,and delay prediction is crucial to improving network determinism.Due to the instability and randomness of the transmission delay sequence,a single model is difficult to predict accurately.To solve this problem,a new delay prediction method based on optimal Variational Mode Decomposition(VMD)and Convolutional Attention Long Short Term Memory Network(CA-LSTM)is proposed.Firstly,in order to improve the decomposition performance of VMD,the correlation coefficient verification method is used to determine the modal number of the time-delay sequence decomposition,and the grasshopper optimization algorithm is used to determine the penalty factor and fidelity coefficient of the decomposition.Secondly,a CA-LSTM network is proposed,which uses convolution filters and an attention mechanism to make the network have the ability to distinguish the importance of time-delay sequences in a certain period.Finally,each modal prediction value is reconstructed into a one-dimensional delay value to get the prediction results.The experimental results show that the optimized VMD can effectively decompose the 5G terminal transmission delay sequence.Compared with the classical LSTM,the CA-LSTM improves the MSE,RMSE and MAE by 37.1％,21.3％and 23.6％respectively.关键词
5G时延/变分模态分解/相关系数/蝗虫优化算法/卷积注意力长短时记忆网络Key words
5G time delay/VMD/correlation coefficient/grasshopper optimization algorithm/CA-LSTM分类
信息技术与安全科学引用本文复制引用
刘壮,盛志超,魏浩,余鸿文,方勇..确定性网络5G-A终端时延预测[J].无线电工程,2024,54(4):1034-1042,9.基金项目
国家自然科学基金(61901254) （61901254）
航空科学基金(2020Z0660S6001)National Natural Science Foundation of China(61901254) （2020Z0660S6001）
Aeronautical Science Foundation of China(2020Z0660S6001) （2020Z0660S6001）
