# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 2 Related Works  
There are a vast number of works in the field of user mobility prediction and mobility-aware services in cellular networks. Generally, user mobility prediction can be categorized in two main parts: (1) mobility models that predict user future path or trajectory (i.e., regression task) and (2) mobility models that predict user next location (i.e., classification task). Our work belongs to the first category and we essentially predict the future trajectory of the user. In the following, we provide a short review of recent works from both categories.  
A huge part of the related works are based on the concept of machine learning algorithms. In [8], the authors introduced a hybrid mobility prediction method using principal component analysis (PCA) and gated recurrent unit. This approach was developed to predict Internet of Things (IoT) mobile users. A destination prediction approach was introduced in [9] using a long short-term memory (LSTM) network. This method investigates user prior knowledge in a bidirectional structure. In [10], a method based on deep learning techniques was adopted for multi-user trajectory prediction. This approach uses LSTM cells to learn a user's pattern and then extends it to the general case. In [11], an LSTM-based model was proposed for destination prediction. This model copes well with data sparsity as well. An RNN-based method was proposed in [12] for trajectory representation learning with the focus of considering both spatial and temporal features in trajectory learning. In [13], the authors proposed a mobility model based on the recurrent neural network variations. They proposed to deploy line simplification techniques to simplify the user trajectory. The core idea is to eliminate irrelevant data to reduce the execution time while improving the prediction accuracy. Then, the preprocessed dataset is used to learn the user mobility behaviour. In [14], the authors proposed a trajectory prediction method. Their technique explores existing relations between a user's morning and afternoon trajectories using a similarity metric. In [15], an analytical model was presented to calculate the handover-related costs including HO latency, signaling overhead, and call dropping. A part of the existing works is based on the popular concept of location-based social networks (LSBNs) that mainly investigates check-in datasets to predict the next point-of-interest (POI). In [16], the authors proposed a context-aware scheme to discover regular patterns in the user's movement history based on RNNs. To solve the data sparsity problem, they analyzed the social relationships as well. In [17], POI estimation was provided by applying gated recurrent units on a check-in dataset.  
Moreover, there are many mobility models based on Markov models and their variations. In [18], the authors proposed a mobility prediction method based on Markov chains, with the main objective of reducing handover-related costs. In [19], the authors proposed a Markov-based model that predicts both a user's path and destination. In [20], a mobility model was introduced to predict the next base station in an LTE network using a Markov model. In [21], a mobility-aware proactive multi-cast technique was presented. This approach deployed a Markov model to estimate user's next cells and staying durations. However, it has been proven that Markov models fail to deal with radical changes in user mobility behaviour and to make a long-term prediction [22]. They cannot predict when hidden states increase. They performed more accurately when there were a limited number of observations.  
We believe that a mobility model should be able to fully analyze the past trajectory of the user to properly predict the future path of that user with the smallest possible error. A model is needed that can perform effectively for both short- and long-term predictions with an acceptable error.