# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 2 Related Work  
Recent research papers have examined the role of 5G networks in facilitating the deployment of V2X technologies, addressing different challenges, proposed solutions, and future work [9; 10]. Handover issues are one of the key challenges that significantly affect the reliability and continuity of V2X communications. To extend the network life and improve its reliability, where the UE has high mobility, a more sophisticated HO algorithm needs to be deployed to detect the context of the network required and the type of UEs and use cases requiring network connection [11]. Responsive HO for hybrid mobility management requires context-aware mechanisms. This context can be used to tailor the fit of resource allocation and management dynamically to the type of UE or V2X application requirements. The UE connection context is crucial for cell selection in V2X to optimize the handover for the following reasons:  
* Devices in a V2X network have mobility patterns that exhibit varying speeds. In addition to knowing the direction and speed of UEs, the connection context helps efficient handover decisions by adjusting the connectivity requirements. * The connection context embodies the required network conditions, data requirements, latency, and reliability that are important to ensure that a handover is performed without interruption to a relevant base station or SC. * The connection context allows handovers to be optimized by anticipating network loads and performance qualities. * The connection context establishes requirements for service quality and the prioritization of applications, ensuring that the handover of UEs is made to highly capable base stations or SC. Generally, it is considered that the connection context in handover is important in guaranteeing that the handover is relevant, reliable, and efficient while catering to the exclusive requirements of UEs with better mobility and user satisfaction. MCDM is considered an effective approach for modeling HO decisions in HetNets, resulting in a reduction in HO problems caused by an improper small-cell selection method [12]. The different MCDM solutions, such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weightage (SAW) [13], and Analytic Hierarchy Process (AHP) [14], are widely implemented to select the most suitable SC for the handover procedure. However, the practice of assigning a fixed weight to each HO parameter in the MCDM method causes the UE to make an inappropriate decision for cell selection in terms that do not reflect the changes in the user resident networks. This, in turn, leads to an increase in the number of handovers, which can add more signaling load to a network [12]. MCDM weights can be classified into three categories: subjective, which relies on expert preference or opinions; objective, which uses mathematical methods instead of expert judgment to determine the relative importance of criteria; and hybrid weighting [15]. In [16], the MCDM was followed to form a context-aware mechanism. AHP, a pairwise comparison approach, is applied to determine the criterion subjective weight based on the UE preference. By utilizing TOPSIS, network criteria and user performance are both involved in selecting the best Radio Access Technology (RAT). Network criteria and user performance are both involved in selecting the best RAT for the connection. Overall, the mechanism eliminates unnecessary handovers by reducing the frequency of handovers. In [17], the MCDM approach is also followed for handover optimization in 5G dense networks. They proposed an enhanced Multi-Objective Optimization Method (MOOR) and a Q-learning method for a two-tier HetNet architecture. To avoid abnormality ranking in the MCDM, the ratio analysis approach (MOORA) merged with the enhanced Entropy objective weighing method and vector normalization instead of summation normalization. The Q-learning method is used to incorporate the most efficient triggering times during the user movement.