Deep Learning Based Traffic and Mobility Prediction

Abstract

This chapter summarizes the background and motivation for why people need to make every effort to predict network traffic and user mobility. To address all the technical aspects related to traffic and mobility predictions with the emphasis on the machine learning based solutions, the chapter first presents the problem formation and a brief overview of existing prediction methods in terms of modelling, characterization, complexity, and performance. It presents some deep learning based schemes for traffic and mobility prediction. The chapter also introduces random connectivity long short‐term memory – a model that reduces computational cost by randomly removing some neural connections – and its performance in traffic prediction. Then it shows three long short‐term memory‐based user‐mobility prediction schemes, two of which take into account the spatial dependence on the user's movement trajectory, thus combining convolutional neural networks to expect to achieve better prediction performance.