# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 2 Related Work
\begin{table}
\begin{tabular}{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 \hline
**Criteria** & \begin{tabular}{c} **Pairwise** \\ **Comparison** \\ **[16]** \\ \end{tabular} & \begin{tabular}{c} **E-MOORA** \\ **[17]** \\ \end{tabular} & \begin{tabular}{c} **OCANS** \\ **[18]** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy-Based MCDM** \\ **[19]** \\ \end{tabular} & \begin{tabular}{c} **CAP** \\ **(Proposed Method)** \\ \end{tabular} \\ \hline Weight method & Subjective & Objective & Hybrid & Hybrid & Dynamic/adaptive \\ \hline Weight assignment & AHP & Modified Entropy & \begin{tabular}{c} AHP and utility \\ function \\ \end{tabular} & FAHP & \begin{tabular}{c} Context-connection \\ policy-based method \\ \end{tabular} \\ \hline Cell selection & TOPSIS & Q-Learning & USI metrics & FTOPSIS & \begin{tabular}{c} Performance index, \\ stay time, load \\ \end{tabular} \\ \hline Adaptability to network changes & \begin{tabular}{c} Yes \\ (based on UE priority) \\ \end{tabular} & Yes & Limited & No & Yes \\ \hline Computation complexity & Moderate & High & High & High & Low \\ \hline Latency & Moderate & High & High & High & Low \\ \hline Heterogeneity & Yes & Yes & Yes & Yes & Yes \\ \hline UDN & Yes & Yes & Yes & No & Yes \\ \hline Limitations & \begin{tabular}{c} Criterion weight \\ based on decision- \\ makingpreference \\ \end{tabular} & \begin{tabular}{c} Computation \\ overhead in dense \\ networks results in \\ more delays \\ \end{tabular} & \begin{tabular}{c} Computation \\ overhead in dense \\ network results in \\ more delays \\ \end{tabular} & \
\begin{tabular}{c} Criterion weight \\ based on \\ preference and \\ more delays \\ \end{tabular} &
\begin{tabular}{c} Optimization handover \\ preference and \\ computation \\ overhead \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparative summary of MCDM-based handover decisions. is its flexible response to changing network conditions and user connectivity requirements. The mechanism uses static network elements such as network topology and dynamic or continuously altered features such as network traffic and mobile node location data to optimize handover decisions, thereby lowering the possibility of missed calls and delays. A network scanning approach chooses when and what to scan depending on the possibility that a handover is required soon instead of continuously searching through available networks. This lessens the energy consumption of mobile nodes. A utility function evaluates the load of network cells, allowing resources to be used more effectively and avoiding bottlenecks that are caused by cells equally distributing the load. Finally, the hybrid channel allocation strategy prioritizes handover traffic by dynamically allocating channels according to the priority of the traffic type, ensuring that vital communications continue uninterrupted throughout the handover process, which is especially crucial in heterogeneous networks because various types of traffic may need different levels of quality of service. In [20], the authors utilized reinforcement learning (RL), based on Q-learning, to optimize handover decisions. Overall, this method improves QoS by minimizing handover failures, reducing ping-pong handovers, and increasing network throughput. Nevertheless, this intelligent algorithm must maintain sufficient iterations to achieve optimal handover decisions, thereby increasing the likelihood of time processing [21] and computation complexity, which is undesirable for high-mobility users such as the V2X network. Recent studies have focused on examining the optimality of cell selection in wireless networks and have suggested different mechanisms with which to tackle the performance degradation caused by handover issues. As listed in Table 2, none of these solutions have specifically introduced the handover challenges faced by V2X users in dense networks while using AR technology.