Deep learning based prediction of traffic peaks in mobile networks

In mobile networks, it is essential to configure networks more efficiently to provide mobile users with services having better quality. For the adjacent cells, sometimes the mobile traffic concentrates in a single cell, leading to traffic imbalance; in many cases, the detection of imbalance is strongly related to the prediction of traffic peaks, which is extremely difficult because many peaks appear suddenly for no apparent reason. To better predict the peaks and traffic imbalance, we propose two novel mobile traffic predictors. The first is a Mixture of Experts (MoE) model which yields significantly better peak prediction along with excellent interpretability by establishing a cooperation mechanism between different experts, whereas the second predictor is a lightweight Multilayer Perceptron (MLP) which can obtain similar peak forecasting performance but operating more flexibly and consuming less computational power. The obtained predictions are then used to aid the predictive detection of traffic imbalance. To this end, we first perform a large-scale analysis of mobile traffic and then propose different approaches to detect the future traffic imbalance based on the predictions. To evaluate the performance of the proposed approaches, we have conducted extensive experiments on real-world mobile network datasets; all models are implemented in Python 3.8.8 with Pytorch 1.7.0, and they were run on a computer with i7-7700K CPU @ 4.2 GHz running Linux, 64 GB memory and a single Nvidia TITAN X Pascal 12GB GPU. The proposed models are compared with 10 widely used deep learning based predictors, and the results show that our approaches have significantly improved the sensitivity (peak prediction accuracy) from 39.3% to 58.5% (i.e., roughly a 50% increase) along with excellent interpretability. Moreover, the predictions are further used by the predictive detection approach, where the best multi-model approach improves the predictive classification accuracy of congestion (imbalance prediction accuracy) by 10.3% in contrast with the naive approach; this result also verifies the effectiveness of our peak predictions.