Mobile Network Traffic Prediction Using Temporal Fusion Transformer

The continuous development of mobile communication technologies has led to a rapid increase in cellular network traffic. Therefore, traffic prediction models have become very important for the design of mobile communication networks, as they are essential for increasing the quality of service (QoS) and ensuring a high level of quality of experience (QoE). Accurate and timely prediction of network traffic volume enables efficient planning of radio resource allocation, improves network energy efficiency, and reduces network congestion and operational costs. However, the task of mobile network traffic prediction is inherently challenging due to the dynamic, multivariate nature of traffic patterns that are influenced by diverse factors such as location, user behavior, and temporal variations. In this paper, we propose a novel prediction model based on deep learning techniques. Specifically, we develop a customized temporal fusion transformer (TFT) for accurate time series prediction that effectively captures the complex dependencies in mobile network traffic and ensures resilience to unexpected variations, which is critical for efficient network management and QoE enhancement. The prediction model is evaluated and tested against state-of-the-art prediction models using real-world cellular network data as the training dataset. The experimental results validate the excellence of this customized transformer architecture in capturing the complex temporal dynamics of cellular network traffic by exploiting attention-based mechanisms.