# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
###### Abstract
Future cell networks require HOs to be set properly since they directly affect the QoS. In ultra-dense HetNets, efficient HO triggering techniques should feature self-optimization to minimize or eliminate network degradation. Self-optimization, which falls under radio resource management, focuses on optimizing the mobility robustness and load balancing. Various techniques for mobility robustness optimization have focused on optimizing HO control parameters [12]. Architecture enhancements for 5G systems (5 GSs) to support V2X services were discussed in 3 GPP Release 16, reporting that self-optimized HO mechanisms can support maintaining seamless connectivity [12]. On the other hand, improper cell selection decisions can lead to several issues that can degrade the overall network services and, therefore, negatively impact user experience. This network deficiency is a result of connection instability when the UE frequently hand-offs connections between small cells (SCs) in a UDN. The deployment scenario, mobility and coverage concepts, and traffic characteristics of SCs for Long-Term Evolution (LTE) networks were proposed in 3 GPP TR 36.932 version 13.0.0 Release 13 [13] to optimize the network performance. Several cell selection methods have been proposed to maintain the handover decision, including the RSS-based algorithm [14], context-aware algorithm [15], cost function algorithm [16], and fuzzy logic-based algorithm [17; 18]. Although these methods demonstrate a level of acceptance in dealing with handover problems in mobile communication, more attention is required when implementing a vehicular network. With rigorous parameters, there are limited opportunities to meet new challenges posed by the complex and dynamic nature of V2X network environments, such as the need for instant decision, increased network densification, and low latency [19]. For instance, the required and more reliable HO decisions can be achieved through the deployment of machine learning (ML). ML paradigms are classified as supervised learning, unsupervised learning, and reinforcement learning (RL). Supervised learning trains labeled data sets to produce patterns, whereas unsupervised learning uses unlabeled ones. RL is a policy learning method in which the essential components are state, action, reward, and punishment for decision-making. This paper presents an enhanced handover optimization method for 5G V2X networks. It integrates a Multi-Criteria Decision-Making (MCDM) approach using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with an intelligent component value-based RL algorithm in the form of Q-Learning. RL is recommended as a better approach to provide a solution for the unaddressed challenge of effective decision-making techniques to accommodate the temporal variation in the vehicular network environment [11; 20; 21]. The main aim is to improve handover efficiency, alleviate network congestion, and optimize performance. The main contributions are as follows:  
1. Develop a dual HO decision method for 5G V2X communications by integrating TOPSIS with a value-based RL algorithm in the form of Q-Learning to provide long-term network stability. 2. Use the TOPSIS method integrated with stay time (ST) and connection context requirements to improve decision quality. 3. The novelty of adjusting the TOPSIS weightage dynamically based on the updated Q-values and accumulated experiences can guarantee achieving better performance for the V2X UDN network. The remainder of this paper is organized as follows. Section 2 provides a review of recent related work, followed by Section 3, which presents the proposed handover Q-Learning-based technique. The results of the proposed solution are presented in Section 4. Conclusions and future work are presented in Section 5.