Abstract:

In the context of the rapid development of mobile communication networks, cellular traffic prediction plays an important role in network planning, optimization and resource management. Taking the complexity and nonlinear characteristics of cellular traffic data into account, a hybrid neural network cellular traffic prediction method based on quadratic decomposition is proposed. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is first used to decompose the original traffic into multiple subsequences, and reconstruct them into frequency and trend sequences by using the K-Shape clustering algorithm. To reveal the underlying structure of data in more detail, the variational mode decomposition (VMD) method is used to perform secondary decomposition on the frequency sequence and generate multi-dimensional frequency sequences. Subsequently, the one-dimensional trend sequence and multi-dimensional frequency sequence are separately input into the local feature extraction module. The single-channel feature extraction layer uses the one-dimensional convolution neural network (1DCNN) method to extract local features of the one-dimensional trend sequence, while the multi-channel feature extraction layer combines the convolutional block attention module (CBAM) method to capture key information in the multi-dimensional frequency sequence. Then we input the extracted feature vectors into the temporal information learning module, and use the bidirectional long short term memory (BiLSTM) model and attention mechanism to complete the output prediction. Finally, we achieve accurate prediction of cellular traffic by summing the predicted results of frequency and trend sequences. To verify the effectiveness of the proposed method, this paper conducts experimental verification by using publicly available datasets and compares the predicted results with various methods. The experimental results show that the proposed method exhibits better predictive performance, which can provide strong support for the management and optimization of cellular networks.

Key words: cellular traffic prediction, modal decomposition, convolutional neural network (CNN), bidirectional long short term memory (BiLSTM) network, convolutional block attention module (CBAM)