# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### UE Velocity-Aware HO Decision Approaches
But for HetNets and dense networks, the network conditions are highly dynamic, where these fixed HCP settings will not work, resultantly requiring an adaptive solution. Similarly, in [83] same authors investigated the performance of different MRO algorithms for 5G networks by considering different mobile velocities. The purpose was to suggest the best MRO approach in dynamic scenarios on the basis of HOPP, SINR and RLF. Table 5 lists the studies discussed in this section, including their parameter selection criteria, HCPs considered for optimization, KPI's used for evaluation, the simulator used, the achievements and drawbacks, and the velocity of the UE. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Ref. No** & **Criteria of Pa-** & & & & \\
**No** & **rameter Selection** & & & & \\
**Year** & & & & & \\ \hline
2021 [76] & Cell Load, UE Velocity, RSRP, SINR & HOM, TTT & HOP, PPHP & OP, MATLAB & Studied the impact of different predefined HCP settings to check the 5G system performance & Compared different predefined UE velocities, HOM, and TTT values & Up to 140 Km/h \\ \hline
2022 [38] & UE Velocity, Cell Load, and RSRP & HOM, TTT & HOP, HOPP, HHT & MATLAB & Reduced average HOPP, HOF, and interruption time by over 90\%, 46\%, and 58\%, respectively & Jumping values may not work well when network conditions change dynamically & Up to 140 Km/h \\ \hline
2019 [85] & RSRP, UE Velocity & HOM, TTT & HOP, RIPP, HDP & RLF, MATLAB & Remarkable reduction in HOPP and RLF by an average of more than 70\% & They used jumped values of HOM and TTT against one predefined conditions that are showing some sort of fixed behavior & Up to 160 Km/h \\ \hline
2022 [38] & Load Balance, Velocity, RSRP & HOM, TTT & RLF, HOPP & SINR, HOPP & MATLAB & Weight function-based optimization and distance-based approaches work best & 40 Km to 200 Km/h \\ \hline
2022 [44] & RSRP, RSRQ, Velocity & HOM, TTT & HOP, HOP, HOL, HIT & MATLAB & FLC based approach compared with [51,87] and improved by 86.78\% and 95.5\% respectivily & Adjustment of Output values with [51,87] and improved by 86.78\% and 95.5\% respectivily & Adjustment of Output values against FLC seems to be fixed for fixed velocity ranges, also UE mobility is limited in specific directions & 20 Km to 160 Km/h \\ \hline
2020 [89] & RSRP, RSRQ, SINR, UE Velocity & HOM & HOP, HOP, HOF, Rate & MATLAB & The proposed model enables to define the best time and the best antenna to perform the HO. The results demonstrate a decrease of up to 43\% in HOPP & HOM is the only HCP and use of other decision parameters can enhance the performance further & 0 to 80 Km/h \\ \hline
2022 [90] & RSRP, RSRQ, Velocity & & & & & \\
2020 [91] & RSRP, RSRQ, Velocity & & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of Velocity-based Handover Approaches. Several velocity-aware HO techniques have been proposed in recent years to improve handover performance in HetNets. UE speed has been used as a decision parameter to modify the HCP values, resulting in reduced HOPPs and improved network performance. The proposed techniques use weight functions, fuzzy logic, and other optimization approaches to adapt HCP values to UE velocity. The results of these studies have shown improved performance in terms of HOPP, RLF, and HOF compared to traditional handover techniques. Distributed optimization strategies have been developed to overcome the limitations of manual optimization and central optimization, resulting in efficient handover triggering and improvement in overall system performance.