Application of GAN Model with DE-GWO Optimized LSTM for 5G Energy Consumption Control

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 algorithm 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 consumption. 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 consumption.

Keywords

• base station traffic
• recurrent neural networks
• generative adversarial nets
• intelligent optimization algorithm
• k-means++ algorithm