现代电子技术2024，Vol.47Issue(23)：125-130,6.DOI:10.16652/j.issn.1004-373x.2024.23.019
基于多头自注意力机制的LSTM-TCN基站流量预测算法
LSTM-TCN base station traffic prediction algorithm based on multi-head self-attention mechanism
李维烨 1贾海蓉 1申陈宁 2吴永强2
作者信息
- 1. 太原理工大学 电子信息与光学工程学院,山西 晋中 030600
- 2. 山西通信通达微波技术有限公司,山西 太原 030000
- 折叠
摘要
Abstract
Base station traffic prediction is crucial for the planning,resource allocation and user experience optimization of cellular networks.An LSTM-TCN base station traffic prediction algorithm that incorporates a multi-head self-attention(MHSA)mechanism is designed in order to improve the accuracy of base station traffic prediction.The MHSA can strengthen the intrinsic correlation of base station traffic data in multiple perspectives,which enhances the model's ability to express important features of traffic data.The long short-term memory(LSTM)network in LSTM-TCN model captures the long and short-term dependencies in the traffic data,while the temporal convolutional network(TCN)captures the global features of the traffic data,which allows the model to extract the change pattern and time dependence of base station traffic data on different time scales,so as to improve the model's fitting ability and prediction accuracy.Experimental results show that the proposed traffic prediction algorithm reduces both the root mean square error(RMSE)and the mean absolute error(MAE)effectively in the prediction of operator base station traffic data and improves the coefficient of determination(R2)in comparison with the other algorithms,which verifies the validity of the traffic prediction algorithm.Therefore,the proposed algorithm can provide decision support for the dormant and energy saving of the base station.关键词
5G流量/基站/流量预测/混合神经网络/多头自注意/LSTM-TCNKey words
5G traffic/base station/traffic prediction/hybrid neural network/multi-head self-attention/LSTM-TCN分类
信息技术与安全科学引用本文复制引用
李维烨,贾海蓉,申陈宁,吴永强..基于多头自注意力机制的LSTM-TCN基站流量预测算法[J].现代电子技术,2024,47(23):125-130,6.
