Attention-based 3DTCN-LSTM short-term network traffic prediction model considering multi-base station spatiotemporal coupling. (22nd February 2023)

• Record Type: Journal Article
• Authors: Zhan, Yuliang; Zhou, Ji; Zhang, Jiayi
• Is Part Of: International journal of Web engineering and technology. Volume 17:Number 4(2023)
• Page Start: 413
• Page End: 435
• Publication Date: 2023-02-22
• Subjects: 3D temporal convolutional network-long short-term memory -- 3DTCN-LSTM -- attention model -- network traffic prediction; World Wide Web -- Periodicals; Web site development -- Periodicals; Application software -- Development -- Periodicals; 006.7
• Languages: English
• ISSNs: 1476-1289

Abstract

Implementing an accurate traffic prediction method can help telecom operators to pre-manage and optimise the network in advance, and it is also easy to adjust the power consumption of the base station. At present, the correlation between mobile devices and local base stations cannot be ignore, to accurately predict network traffic. Combining the spatiotemporal characteristics of traffic to achieve more accurate traffic prediction, this paper proposes a 3D temporal convolutional network-long short-term memory (3DTCN-LSTM) model optimised based on the attention mechanism. The mechanism reduces redundant information interference, enabling the extraction of long-range spatial correlations. The long-term dependency characteristics of the traffic are then obtained through the LSTM network. Finally, the experiments on the dataset demonstrate that the prediction effect of the 3DTCN-LSTM model is significantly better than LSTM, BiLSTM, TCN, TCN-LSTM, 3DCNN and other models.