# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 1 Introduction  
Nowadays, there is a significant increase in the number of mobile users around the world. It is estimated that this number will dramatically increase up to 5.7 billions by 2023 based on the Cisco annual record [1]. Consequently, a considerable amount of mobility data is generated by these users while moving everyday. We can fully exploit the potential of these produced data in many applications in order to extract frequent patterns and to derive invaluable insights. This fact opens up plenty of golden opportunities in many prospective areas such as mobility-aware services. User mobility prediction techniques can be applied to these available data to obtain a deeper understanding of users' mobility behaviours and their demands by using data mining and learning algorithms. Foreseeable future mobile communication networks are rightly expected to support high data rates and seamless connectivity for a vast number of devices. They are also expected to avoid lengthy delays when providing services to mobile users in the network. However, there are some inherent technical challenges in mobile networks that require special attention when providing a high quality of services (QoSs) for users [2]. User mobility can pose tremendous challenges and needs to be managed appropriately. Some of these problems are heterogeneity, more frequent handovers, increase in handover-related costs, and increase in call dropping probability [3]. Therefore, it is crucial to have proper mobility management in the network. A self-organizing network (SON) is a potential long-term solution to cope with some of the mentioned underlying issues [4]. These networks are growing greatly in popularity due to the fact that they are able to dynamically adapt to changes and to learn from past experiences. A SON is composed of three main functions: self-configuration, self-optimization, and self-healing. In the context of intelligent mobility management, user mobility prediction is a subsection of self-optimization functions with the main objective of predicting the future location of the user using learning techniques. Mobility awareness can make potentially outstanding contributions to many research areas related to mobile network management including resource allocation, handover management, and location-based services and, in general, can increase the quality of services. When a user moves between different cells, the procedure of reassigning user resources to a new base station is called handover (HO). Handover is triggered based on the received signal condition or network load balancing. In dense 5G cellular networks, user mobility results in more repeated handovers and service interruptions. This can cause high signaling overhead, especially for mobile users at high velocities. Hence, handover management has a leading role in cellular network performance in order to support seamless connectivity for users. Therefore, improving HO procedure performance can be highly beneficial in reducing link failures, latency, and costs in a cellular networks. Modeling user mobility can significantly help the network obtain prior knowledge about a user's future trajectory and handovers. Therefore, deploying predictive handover management, the network can prepare the required services for users in advance to reduce latency and costs [5]. Having predicted the future HO of the user, handover preparation steps can be performed beforehand, and when HO is needed, the process can start from the execution phase. This can reduce the number of signaling messages needed to be exchanged and consequently results in lower HO latency. There are a large number of works related to user mobility prediction and mobility-aware services for cellular networks in the literature. Despite valuable efforts, a generic reliable mobility predictor with a low prediction error for short-term and long-term predictions with low complexity is still missing.