WVETT-Net: A Novel Hybrid Prediction Model for Wireless Network Traffic Based on Variational Mode Decomposition

Abstract

Precise prediction of wireless communication network traffic is indispensable in the operational deployment of base station resources and improvement of the user experience. Cellular wireless network traffic has both spatial and temporal characteristics. The existing modeling algorithms have achieved good results in extracting the spatial features, but there are still deficiencies in the extraction models for the time dependencies. To resolve these problems, this paper proposes a novel hybrid neural network prediction model, called WVETT-Net. Firstly, variational mode decomposition (VMD) is used to preprocess network traffic, and the whale optimization algorithm (WOA) is used to select the optimal parameters for VMD. Secondly, the local and global features are extracted from each subsequence by a temporal convolutional network (TCN) and an improved Transformer network with a multi-head ProbSparse self-attention mechanism (Pe-Transformer), respectively. Finally, the extracted feature representation is enhanced by using an efficient channel attention (ECA) mechanism to achieve accurate wireless network traffic predictions. Experimental results on two wireless network traffic datasets show that the proposed model (WVETT-Net) outperforms the traditional single or combined models in wireless network traffic prediction.