## 2 Related Works
### Machine Learning Based Handover Management
The proposed CNN-LSTM model extracted features from historical trajectories to predict the direction of UE actions based on the historical trajectories of the UE. They selected a certain range of user trajectory data and set the time span of the data to no more than 5 s in order to better predict user trajectories for small coverage of 5G BS. For every 5 s, these data were recalculated as new path data. The simulation results showed that the proposed model could reduce the number of BS handovers by predicting the trajectory of the user. The CNN-LSTM was a more effective and stable model in predicting the trajectory of the user. However, we believe that the selected prediction time range cannot be based merely on a single prediction time range. It may not be sufficient to meet all demands of a handover decision in a shorter prediction time when the UE is moving fast. Therefore, in our research, we conduct the different prediction times, 2, 4, 6, 8 and 10 s as the basis of the experiments to meet the requirements of various UE moving speeds. In [9], the authors proposed and evaluated the performance of a machine-learning-based approach to handover in scenarios with the presence of signal-blocking obstacles. The ns-3 simulator was used as the proof of concept. In the proposed mechanism, UE could choose the eNB that would most likely offer users the highest long-term QoS after the handover procedure, even in severe propagation conditions. The proposed schemes presented higher performance as compared to the classical ones and substantially improved users' QoS in challenging scenarios. In [10], an intelligent handover scheme based on the Elman network in high-speed rail scenarios has been proposed. A neural network model in different regions, as well as input parameters, was trained by the Elman network to obtain the good prediction capabilities in order to improve the effectiveness and reliability of handover. A handover mechanism for cost assessment was proposed in [11] that integrated signaling overhead, delay, call drops, and radio resource. The mathematical model developed was applicable to a variety of cellular architectures, but the focus of the paper was on the control/data separation architecture (CDSA). In addition, the handover prediction was proposed and evaluated as part of the overall cost through the stacked long and short-term memory (LSTM) model. Their evaluation of the overall model across different prioritization parameters such as user dissatisfaction, latency, signaling strength, and resources showed the impact of prediction accuracy as part of the overall cost. They also showed that the de veloped model coupled with a complementary data-driven deep-learning approach could reduce user complaints, latency, signaling overhead and resource waste. They concluded that the performance improvement could be attributed to the inherent recurrent nature of the LSTM model, which favors better model time series data. However, the priority parameters proposed in their paper should be specific to the network services used by the user, rather than fixed parameters, especially for the scenarios of users conducting services such as real-time video stream services. In this case, the users are more sensitive to the network quality requirements. Therefore, our research will provide a deep learning model to provide a more efficient handoff mechanism for users when using Skype real-time video conference services. In [12], the authors proposed an offline scheme based on double deep reinforcement learning to minimize the frequency of handover in mm-wave networks. The optimal base station selection policy maximized the UE-BS connection time from the offline DDRL algorithms. The authors in [13] employed the LSTM model to simulate a regression problem to estimate the expected time for different neighbors of the current service cell while downloading a file. The proposed approach was better than the traditional event-based handover scheme.