太原理工大学学报2024，Vol.55Issue(4)：743-750,8.DOI:10.16355/j.tyut.1007-9432.20230140
改进GAN模型在基站流量预测及5G节能中的应用
Application of GAN Model with DE-GWO Optimized LSTM for 5G Energy Consumption Control
摘要
Abstract
[Purposes]In order to predict the traffic of 5G base stations more accurately and analyze the tidal phenomenon,a traffic prediction method of GAN model with differential algo-rithm is proposed to improve the gray wolf optimized LSTM.And the modified GAN model is used in the timing control of actual base stations,which can effectively save the energy consump-tion.[Methods]First,since GAN is not adaptable to while LSTM is suitable for time series problems,by combining them,the GAN generator optimizes LSTM by differential evolution grey wolf algorithm.Discriminator uses GRU for discriminating,through continuous adversarial training,the generator and discriminator get equilibrium,thus improving the prediction accuracy of 5G base station traffic.Second,because of the poor global search capability of k-means++al-gorithm,the k-means++algorithm is optimized by using an improved artificial bee colony,and is used to output the optimal base station timing time to achieve the maximum energy saving.[Findings]The experimental results show that the proposed model has higher prediction accuracy compared with existing models,and the timing control function can greatly save energy consump-tion.关键词
基站流量/改进循环神经网络/GAN网络/智能优化算法/k-means++算法Key words
base station traffic/recurrent neural networks/generative adversarial nets/intel-ligent optimization algorithm/k-means++algorithm分类
计算机与自动化引用本文复制引用
王素英,贾海蓉,申陈宁,吴永强,刘君..改进GAN模型在基站流量预测及5G节能中的应用[J].太原理工大学学报,2024,55(4):743-750,8.基金项目
国家自然科学基金资助项目(12004275) （12004275）
Shanxi Scholarship Council of China(2020-042) （2020-042）
山西省自然科学基金资助项目(20210302123186) （20210302123186）
