# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Machine Learning/Deep Learning Based HO Approaches
In [103], an online reinforcement learning technique was developed that uses Kalman filter to compute the best of the serving and target cells' RSRP values. Based on the projected signal quality, a state-action-reward-state-action (SARSA) based reinforcement scheme was employed for target cell selection which results in a dynamic handover decision that considers future network conditions. They also used an epsilon greedy method to dynamically change the TTT and HOM levels based on the user's requirement. They considered different UE speed scenarios for simulation and checked the scheme for throughput, PLR, packet delay, HOL, HOF and error rate. In [166], the authors proposed a recurrent neural network-based trajectory prediction mobility model. The fundamental concept is to reduce complexity by eliminating raw data and improving the accuracy of trajectory prediction to learn the user's mobility behavior. The authors of [167] proposed a user mobility prediction scheme for HO management in Cellular networks by acquiring user mobility data. After acquiring mobility data, they utilized a hybrid deep learning algorithm known as vector autoregression and gated recurrent unit (VAR-GRU) to estimate the UE's future trajectory. The proposed method is subsequently evaluated in terms of future mobile location error, HO cost, and HO processing cost. In [168], they proposed an LSTM-based framework for single and multi-user UE trajectory prediction. They employed a fundamental framework for single-user trajectory predictions and a sequence-to-sequence framework for cluster-based multi-user trajectory predictions. The simulation outcomes compared single-user and multi-user prediction strategies. Prediction of UE's future location can result in improved estimation and performance in handover procedures, thereby improving HO-related issues. The authors developed a deep neural network-based proactive conditional handover scheme for 5G dense networks in [147]. As 5G networks are more prone to blockages and obstacles, a robust approach that can bear the sudden signal changes is required. With conditional handovers, multiple target BSs prepared in advance for handovers even when the connection between the UE and its serving BS is still reliable. The proposed proactive deep learning approach compared, different blockage densities; however, during the preparation phase, the target BSs have to reserve radio resources for the UE, which will cause inefficient resource utilization. The authors of [113] employed RL algorithms to predict the proactive handover decision-making based on pedestrian movement data. They used the location and mobility rate of pedestrians to determine the appropriate handoverpolicy for maximizing the system's throughput. They collected the position and velocity of the users using an RGB camera. On the basis of the obtained data, a Q-learning algorithm was implemented and a handover decision was made. The simulation results demonstrate that the Q-learning-based handover prediction is superior to the current heuristic handover decision-making in terms of obtaining higher throughput against service disruption times. The authors of [114] proposed a cognitive self-optimization approach by using a fuzzy-based Q-learning algorithm along with a load difference method to optimize the load balancing issues. According to the authors, these cognitive approaches can perform better for unknown parameter relationships as compared to traditional rule-based or command-based systems in which each rule has to be defined separately for every possible scenario. They compared the Q-learning framework with traditional approaches at different speed scenarios for throughput, the number of unsatisfied users, and energy efficiency. Similarly, this research [169] also emphasized that proactive and cognitive approaches based on deep learning can assist mobile networks to provide optimal services to end customers without sacrificing quality. They used two distinct approaches for predictive and non-predictive handover scenarios.