# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis
#### 4.3.1 Performance Comparison in Terms of Varying UE Velocity  
The velocity of the UEs significantly affects the HO performance in a 5G V2X network. Increasing the velocity raises UE transitions from cell to cell, leading to degraded network performance. HOs at lower speeds are usually stable, but higher speeds result in an increased probability of handover issues, such as HO failure, unnecessary HOs, ping-pongs, and noticeable delays. Figure 3 illustrates a UE movement [X,Y] at different velocities in a network environment dimension of a (100 \(\times\) 100) network size for the proposed algorithm (IHD-V2X). The most suitable cell for handover decision sample output for a UE traversing at random velocities is presented in Table 5.  
The most suitable cell for handover decision sample output for a UE moving at random velocities is presented in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline Mov ID (M): **4** & \multicolumn{3}{c}{Maximum Load: **20**} \\ Sector ID No (Sec\({}_{n}\)): **24** & \multicolumn{3}{c}{Velocity (V): **117.84**} \\ No. of SCs in Sec\({}_{n}\): **30** & \multicolumn{3}{c}{Context: **Audio**} \\ \hline
**SC** & PI & SC load & ST & status & Action \\ \hline
**SC9** & 0.258674345 & 15 & 1 & Necessary \\ \hline
**SC3** & 0.296381396 & 8 & 0.9915143 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC21** & 0.308723826 & 9 & 0.9830286 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC23** & 0.309375018 & 16 & 0.9745429 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC26** & 0.375878704 & 3 & 0.9660573 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC1** & 0.4069305 & 5 & 0.9575716 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC5** & 0.409621584 & 4 & 0.9490859 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC16** & 0.418339327 & 6 & 0.9406003 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC29** & 0.428719489 & 10 & 0.9321146 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC19** & 0.435141813 & 5 & 0.9236289 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC4** & 0.457167959 & 13 & 0.9151433 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC14** & 0.521482773 & 18 & 0.9066576 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC22** & 0.53638102 & 20 & 0.8981719 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC11** & 0.55055526 & 4 & 0.8896862 & \multicolumn{3}{c}{Unnecessary} \\ \hline
**SC12** & 0.582608246 & 8 & 0.8812006 & \multicolumn{3}{c}{Unnecessary} \\ \hline \end{tabular}
\end{table} 
Table 5: HID-V2X sample output of the handover decision for a UE moving at random velocities. Figure 3: Sample scenario of a network map for a UE moving at random velocities. It is observed that when the velocity increased, the number of HO attempts increased noticeably for all algorithms. ACHQ shows the greatest number of HO attempts across all velocities, resulting in unnecessary signaling expenditures. CAP effectively decreases unnecessary HOs by ensuring a minimum stay time before transitioning to other SCs. The best performance was observed with TOPSIS with ST and Q-Learning, the IHD-V2X approach, which further reduced the number of attempts by learning from previous HO experiences and predicting optimal handover moments. Meanwhile, ACHQ somewhat improves the success rate but is unable to adapt to fast mobility and suffers from a significant drop in successful handovers, falling below 50% at 140 km/h. The failure rate worsens due to inefficient HO decisions and unnecessary HO attempts. CAP further improves the success rate by stabilizing connections before triggering an HO. IHD-V2X achieves the highest success rates, exceeding 80% for the necessary HOs, by adapting to varying velocities and dynamically tuning HO decisions. Handover delays have become a serious issue, mainly for ACHQ, resulting in excessive delays due to inefficient HO execution. CAP reduces delays by optimizing HO timing and averting recurrent disruptions in connections. The best performance is shown by IHD-V2X, which minimizes HO delays through machine learning to predict and optimize HO execution, even at higher velocities.