Cellular Network Traffic Prediction Based on Correlation ConvLSTM and Self-Attention Network

Abstract

Predicting the future dynamicity of the network traffic are crucially important to support the 5G intelligent system and automated network management. In this letter, we propose a Correlation-based ConvLSTM and Self-Attention-based Network (CCSANet) to accurately predict complex cellular network traffic. In the proposed CCSANet, the correlation layer is leveraged in ConvLSTM to improve the ability of extracting consecutive spatial features. Additionally, the self-attention is adopted to aggregate the ability of extracting the dependency between external factors feature and network traffic feature. Experimental evaluations on real-world cellular network traffic datasets demonstrate the effectiveness of CCSANet, which outperforms the state-of-the-art (SOTA) methods.