Embedded Network Communication Accurate Prediction of Power Consumption for Remote Data Transmission
As Internet applications gradually cover various fields,in order to make embedded network communica-tion systems have higher performance and reliability.Memory and power requirements are increasing during data transfer.In order to meet the above requirements,this paper uses convolutional neural network as a data prediction method,and combines GINI index classification to predict the power consumption of accurate long-distance data transmission.Firstly,the discrete wavelet transform is used to discretize the continuously transmitted signal to improve it.Then,the accurate GINI index classification algorithm of power consumption analysis is used to divide the discrete data into categories.Finally,the CNN network is used to learn the complex mode and feature capabilities of power consumption data,and the power consumption of the convolutional neural network(CNN)is introduced for modeling and prediction,which improves the accuracy of predicting the power consumption of remote data transmission.The simulation results of the long-range data transmission power prediction algorithm proposed in this paper show that the propagation rate of the algorithm is significantly improved,reaching a transmission rate of 118.72 Mbps,and the algo-rithm effectively increases the volume of data transmission,and the capacity increase rate is as high as 96.79%.This study not only improves the accuracy of prediction,but also significantly improves network performance and data transmission efficiency,which has important research value in the field of power consumption prediction.The research is key to meeting growing communication demands and improving network reliability.
万方数据
维普
