Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

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%.