# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Machine Learning/Deep Learning Based HO Approaches
This algorithm takes into account multiple input parameters to determine the most desirable network preference between LTE and WLAN for handover. By using this algorithm, it's possible to maintain QoS during the handover process. Additionally, the authors proposed an intelligent interface activation technique that uses an RNN model to predict network conditions and reduce unnecessary network measurements during excellent network conditions. This technique optimizes power consumption in mobile terminals, further enhancing the user experience in HetNets. The authors in [164] proposed a handover algorithm for next-generation multi-tier cellular networks. The proposed algorithm utilizes a look-up table, DL, and LSTM approaches to improve user QoE and QoS requirements while reducing HOF and ping-pong rates. Simulation results show that the proposed algorithm outperforms the current 3 GPP scheme, with a significant increase in overall network throughput 40 to 60% and a reduction in HOF and ping-pong rates by 30% and 86%, respectively. These promising results suggest that the proposed solution could effectively enhance handover in next-generation multi-tier cellular networks. The authors of the article [165] proposed an adaptive approach for cell selection in two-tier HetNets using SDN and ML. The proposed adaptive algorithm, called adaptive two-tier based on the K-Nearest Neighbor (A2T-KNN) algorithm, intelligently selects the best BS from the two-tier BSs based on vehicle information and the features of the HetNet. The results demonstrate that the proposed algorithm outperforms other related schemes in terms of the average number of handovers, achieving up to 45.83% improvement. Additionally, the proposed algorithm improves the average achievable downlink data rate and network energy efficiency achieved by up to 17.18% and 16.86%, respectively. In [93], a transfer learning (TL) and RL based robust optimization approach was proposed for small cell networks. The TL-based first part of the approach was used for  
Figure 10: Schematic Diagram of Machine Learning/Deep Learning-based Handovers in HetNets. network topology adoption in a centralized environment. Whereas the RL-based second part was used for HCP parameter optimization. In the topology adaption phase, they check the topology for any change in previously available information (Similarity detection). If there is no change, the previously selected HCP values will be forwarded to the topology. And in case of any change, the newly estimated HCP values will be forwarded and new topology information will be stored in the database for the next iteration. Geometry-based computation for CIO's upper and lower bound values was also established, representing a maximization function of Too-Early HO (TEHO) and HOPP and a minimization function of Too-Late HO (TLHO). Lastly, RL based HCP adaption algorithm uses prior knowledge of the parameters and fine-tunes them to an optimal value. They optimized the HOM, TTT and CIO values and compared the results on the basis of RLF and HOPP. In [98], the authors developed an approach based on Q-learning (QL) algorithm to perform self-optimization in LTE networks. The basic purpose of this approach was to perform optimal handover configurations independently. For this, the author has used an epsilon-first learning algorithm for cell selection. However, for distributed self-optimization, this single learning agent has to be used on a shared learning policy in which different actions are collected separately from these single agents and forwarded into a central Q-Table. They created TTT and HOM tuples according to UE velocity and divided velocities into thirteen groups. The change in velocity will be observed and depending on this change, the suitable action or values will be selected from the centralized Q-Table. After this, the behaviour against performed action will be monitored, and accordingly, the reward will be updated. The simulation results were compared for cell load, user satisfaction, HOPP, HOP and RLFs.