Transformer-Based Spatio-Temporal Traffic Prediction for Access and Metro Networks

Predicting the traffic in metro and access networks has a significant influence on expanding and upgrading the network. However, the intricate temporal and spatial correlations in such networks are affected by the users' behaviour and geographic characteristics. Consequently, predicting network traffic in metro or access networks faces significant challenges. To enhance the accuracy of this prediction, this paper explores a spatio-temporal approach to predicting traffic, based on a transformer model. This model effectively captures the temporal correlations in the traffic through a multi-head attention mechanism, learning long-term correlations resulting from the behaviour of the users of the individual nodes. Additionally, it acquires the spatial correlations of the traffic through a graph convolutional layer to capture short-term bursty correlations between users. Unlike previous single-point predictions, we optimize the model to make collaborative predictions for all the network nodes. Through parameter optimization, the improved model accurately predicts network traffic in small-scale regions. We implemented simulations using OMNET++ and predicted the traffic in various scenarios. Concurrently, we validated the proposed method with real network traffic based on operator measurements, achieving a prediction accuracy of 98.49%.