A Meta-Learning Based Framework for Cell-Level Mobile Network Traffic Prediction

In this paper, we propose a meta-learning based cell-level network traffic prediction framework (ML-TP), which can provide the proper initial weight vector for the learning model of a new prediction task based on the prediction task's meta-features. In the ML-TP, each prediction task forms a base-task, and a multi-layer long short-term memory (LSTM) network is constructed as its base-learner. Through fast Fourier transform (FFT) analyses of real-world network traffic data, we find that the five most dominating frequency components can capture the cell-level traffic variations, and hence can be used as a base-task's meta-features. We prove that the well-trained weight vector of a previous base-task's base-learner is likely to be a proper initial weight vector of a new base-task's base-learner if the meta-features of the two base-tasks are close to each other in the Euclidean space. Accordingly, we propose a K-nearest neighbours (KNN) algorithm based meta-learner to deal with the meta-task in the ML-TP. Numerical tests show that the ML-TP can significantly increase the base-learners' after-training prediction accuracy and learning efficiency in terms of the number of base-samples and the number of epochs needed in each base-learner's fine-tuning progress.