# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Values/Ranges**} \\ \hline Size of the area & (100 \(\times\) 100) km\({}^{2}\) \\ Network sectors & 25 \\ No. of SCs in each sector & 30, 50, 100 \\ No. of UEs & 10, 20 users per sector \\ UE speed & 20–100 km/h \\  & -  RSSI range for 5G NR (–120 dBm to \(-\)13 dBm) \\ Connection quality & -  SINR range (0–127 dB) \\ parameters & -  BER 10-9, 10–13 ratio \\  & -  Data transmission rate (100–400 Mbps; mid-band 5G) \\ Connection quality & \multicolumn{1}{c}{} \\ Parameter weights & \multicolumn{1}{c}{} \\ Maximum distance & 19 km \\ Data rate & 100–400 Mbps; mid-band 5G \\ V2X 5G E2E latency & 7.8 ms \\ User connection traffic classes & Audio, Video, General, AR, DataOff, Relay transmission. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The setting value of the experiments. It is evident that CAP outperforms the traditional method, resulting in minimal HO issues. Consequently, it is a better option than conventional handover systems and produces increased service quality, increased efficiency, and improved overall user experience. The performance of the proposed method is also compared with that reported in [8], as shown in Figure 5. The ACAVHD method serves as a context-aware state-of-the-art method for HetNets that is utilized to evaluate and compare the performance of the CAP mechanism. The basic traffic class for handover calls (Audio, Video, and General classifications) is considered in the ACAVHD algorithm. The AR, data offloading and Relay traffic profiles were added to ensure the fairness of the method comparison. It displays the average of each user-connection traffic class obtained during the simulation of 2000 different running iterations while maintaining the number of UEs and SCs per sector at 20 and 30, respectively. The UE velocity varies from low to high. The simulation results convincingly show that the CAP method significantly reduces handover issues for all the services in comparison to the ACAVHD method. Figure 4: Comparison between CAP and traditional HO methods. Figure 3: Applications specific for each parameter based on UE preference. Figure 5: _Cont._  
In CAP, HO attempts to show the frequency of handovers segregated for each traffic class while the UE moves from one location to another. The highest average for the number of handovers was attempted for all traffic when the UE moved at a speed of 100 km/h. Compared to other traffic classes, Relay is the most active. This is crucial for V2X to facilitate seamless connectivity. The proposed method's impact is evident in the decreased perception of link failure and the delay of an average of 1% for all traffic types. The accepted value for ping-pong on average is 6% due to the high percentage of 84% that prevents unnecessary handover. In contrast, the ACAVHD method demonstrated low efficiency: the traffic is almost equally distributed among the services. It clearly demonstrates that the CAP method reduces the frequency of handover by nearly 99% compared with the compared algorithm. On the other hand, the CAP achieved a lower ping-pong rate, which is proportionally related to the prevention of unnecessary handovers, where the higher the number, the better. This is a result of avoiding unwanted handovers (necessary HO) as an implication of the high-ping-pong HO. This phenomenon can cause high signaling in a network and inefficient resource utilization, which, in turn, affects network efficiency. The inclusion of application-specific parameter-weighted factors in the TOPSIS method guarantees that all the user information context contributes to the HO decision, thereby enhancing the operation of services' traffic.