Media Information Dissemination Model of Wireless Networks Using Deep Residual Network

Abstract

Information dissemination and its prediction in wireless networks is a challenging task. Researchers have studied the prediction process of media information dissemination in wireless networks using various methods. In this paper, we analyze information dissemination in wireless networks using a deep residual network model. In the proposed model, the relative weight of nodes and the dissemination probability of media information in wireless networks are obtained. The obtained information is the inputs into the deep residual network as features. The convolution feature extractor is used to obtain the details of the input features. Finally, the propagation information is classified according to the extracted features through the full connection layer. We have used the SELU activation function to optimize the deep residual network. In this way, a complete media information dissemination prediction of wireless networks is obtained. The simulation results show that the proposed model has fast convergence and a low bit error rate of information dissemination. It reflects the characteristics of media information dissemination in a wireless network in real-time applications. The results show accurate prediction of media information dissemination in wireless networks.