# 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 4G and 5G network paradigms, handover management is a critical challenge in small-cell HetNets and can negatively impact the QoS and QoE with an increase in handovers. To address this challenge, deep learning and machine learning have been adopted as effective solutions for managing mobility and handovers by optimizing HCP parameters. These techniques have gained popularity over traditional optimization procedures due to their robust optimization and convergence properties. As demonstrated in Figure 10, advanced Machine/Deep/Reinforcement Learning strategies can be utilized to optimize handover procedures in HetNets. Several articles that have utilized ML and DL methods to tackle handover issues are highlighted in this subsection and summarized in Table 9.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Ref. No Year** & **Criteria Parameter Selection** & **of HCP’s** & **KPI’s** & **Simulator** & **Achievement** & **Drawbacks** & **Algorithm** \\ \hline
2020 [105] & SINR, E2E Delay, HOP, Päter Loss & Output (Yes/No) & Delay, HOP, Stay Time, Throughput & Pothon & Reduced handover delay to 11.37 ms, increased connection/stay time to 7.79 s and throughput to 12.72 mbps & Shows poor performance for the seamless connectivity between a sequence of neglected handover input parameters & APBSO based Autoencoder \\ \hline
2021 [110] & UE Velocity, Location & & Throughput & PSO-NN hybrid approach improved throughput upto 34.34\% as compared to previous works & Hybrid of PSO-NN \\ \hline
2022 [106] & TTT, threshold, old1, thresh-old2 & RSRP, SINR, HOSR (HOP) & Synthetic-NET & Data driven approach having root mean square error of 0.063 dBm, 0.1995 dB and 2.99% for RSRP, SINR and handover sources rate respectively, modified GA further improves performance by 21 times faster & Synthetically generated data may not exhibit much better grid to real-time scenario & XGBoost with modelified GA \\ \hline
2020 [120] & CIO, HOM & SINR & With limited data availability CatBoost achieved RMSE of 1.144 dB for SINR, GA further enhance the performance by finding optimal values & May require a large dataset to reach a sufficient accuracy also the optimization of TTT predicts more optimal results & CATBoost with GA \\ \hline
2022 [118] & RSRP, UE Speed, UE Data Type & No. of Hos, Energy Consumption & No. of Hos, Energy Consumption & MATTAB & Compared to the PSO, the PSOC reduced handovers by 20\%. & PSO and and PSOPC \\ \hline
2022 [2021] & Security index, latency, QoS, RSRP & HO, DCD, Throughput, Energy Consumption & ND-2 & Improved energy consumption, delay, call drop, throughput with the values of 0.053599, 0.0142 sec, 0.0628 sec, and 104.58 kbps respectively & GWO and \\ \hline
2022 [2014] & Location, Disculec, Velocity of UE, Position of UE & Delay, No. of HOs, Throughput, HOF & MATTAB & MATTAB & Further investigation about the scheme may be required through other handover related parameters & Hybrid of CS and GA \\ \hline
2020 [107] & TTT, th1, th2 & RSRP, HOSR & Synthetic-NET & An optimization approach used XGBoost that predict HOSR and RSRP with RMSE of 2.5\% and 0.074 dBm respectively, GA further enhanced the results & XGBoost with GA \\ \hline
2021 [97] & RSRP, UE, Velociy, Load & TTT, HOM & HOF, HOPP & TTT, HOM, and MLB should be adjusted based on UE speeds  
The authors of [161] suggest using AI-assisted mobility predictors to manage a dense 5G network, aiding in resource allocation, load prediction, and energy saving. XGBoost was found to be more accurate in predicting mobility, achieving up to 95% accuracy with new input features, a realistic mobility model, and HetNet with shadowing. Similarly, [162] proposes an XGBoost algorithm to improve handover success rates in a sub-6 GHz LTE and mmWave HetNets, improving session maintenance and meeting the demands of 5G. To address the challenges of HetNets, the authors of [163] proposed a fuzzy logic-based vertical handover decision algorithm.