A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Abstract

Future mobile networks are envisioned to have critical limitations in terms of latency, energy usage, capacity and network resources since these networks are expected to become extremely dense and complex. The rapid enormous advances in recent technologies such as Internet of Things (IoT) highlights the urgent need for network performance enhancement as well. To this end, self-organizing networks are a promising solution to push the network performance to the next level. These scalable networks can dynamically adapt to possible changes in the network. Smart mobility management, in particular mobility prediction, is a subsection of self-organizing functions which are mainly based on the machine learning techniques. In this paper, we propose to estimate user’s future trajectory using machine learning approaches for a better network management. We propose a novel bidirectional trajectory prediction model called BTPM to model the user mobility behavior. The proposed method exploits the potential benefits of bidirectional gated recurrent unit (GRU) for having an accurate prediction. Moreover, we introduce a data preprocessing phase to obtain better results with significantly lower execution time. The proposed approach takes full advantage of data analysis in both directions (backward and forward) in order to provide a long-term prediction and model user’s mobility even with complex patterns. Experimental results show that the proposed bidirectional approach significantly improves the performance of the mobility predictor in terms of model accuracy, robustness and execution time. It achieves a model error of 0.014 and decreases the execution time up to 97%.

Keywords

• Recurrent neural networks
• trajectory prediction
• mobile networks