# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
###### Abstract  
Fifth-generation Vehicle-to-Everything (V2X) networks have ushered in a new set of challenges that negatively affect seamless connectivity, specifically owing to high user equipment (UE) mobility and high density. As UE accelerates, there are frequent transitions from one cell to another, and handovers (HOs) are triggered by network performance metrics, including latency, higher energy consumption, and greater packet loss. Traditional HO mechanisms fail to handle such network conditions, requiring the development of Intelligent HO Decisions for V2X (IHD-V2X). By leveraging Q-Learning, the intelligent mechanism seamlessly adapts to real-time network congestion and varying UE speeds, thereby resulting in efficient handover decisions. Based on the results, IHD-V2X significantly outperforms the other mechanisms in high-density and high-mobility networks. This results in a reduction of 73% in unnecessary handover operations, and an 18% reduction in effective energy consumption. On the other hand, it improved handover success rates by 80% from the necessary handover and lowered packet loss for high mobility UE by 73%. The latency was kept at a minimum of 22% for application-specific requirements. The proposed intelligent approach is particularly effective for high-mobility situations and ultra-dense networks, where excessive handovers can degrade user experience. energy consumption; handover; latency; packet loss; Q-learning; V2X 2025  
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public safety UEs communications [2], is used for V2X Sidelink, but improved techniques are required to ensure Cellular-V2X communication. The current Radio Access Network (RAN) technologies for V2X communication are compared in Table 1.  
Enhanced models of information exchange between vehicles using V2X can make travel much safer, reduce road congestion, and maintain eco-environments. The first launched vehicular network was limited to the objective of road safety, in essence, to control the number of deaths from traffic collisions. The vehicles can exchange information about their speed, position, and direction to avoid collisions and prevent accidents. Vehicles can also receive traffic signal timing information to optimize speed and reduce stops at intersections. Nowadays, more use cases have been considered for this technology. The advanced use cases for V2X include remote driving, intelligent platooning, unmanned aerial vehicles (UAVs), and satellite communication networks [3]. Focusing on achieving the potential of V2X not only helps drive the evolution of informatization. The implementation of V2X will promote industrialization by transforming and upgrading the automotive industry to an enabler/provider of technology support for the realization of smart cities and smart environments that foster safe, comfortable, and intelligent urban living. With the advent of new vertical industrial applications such as autonomous vehicles, railways, and unmanned aerial vehicles (UAVs), the deployment of higher-density heterogeneous networks was touted as the ideal solution to accommodate the rapidly increasing quantities and dynamic user demands in cellular networks [4]. However, the dynamic nature of V2X communications and the variety of Quality of Services (QoS) required for its applications resulted in frequent changes in network topology and consequently short connection timing. The current mobility management method, reliant on signal strength, is not effective for diverse V2X QoS requirements [5]. It is necessary to determine a solution that could optimize the mobility management of V2X UEs, first and foremost, to effectively address issues encountered in frequent connection changes in heterogeneous network environments, particularly in the network/cell selection. Cell selection in a cellular network is the process of establishing a connection between a vehicle or user equipment (UE) and the network. The process of selecting cells in 5G networks is influenced by significant improvements and considerations compared to previous generations.