# Handover Management in 5G Vehicular Networks
## 1 Introduction  
Fifth-generation (5G) vehicular networks have emerged rapidly. In a typical 5G vehicular network architecture, vehicular equipment includes On-Board Units (OBUs) [1] and mobile User Equipment (UEs). Vehicles communicate with each other, as well as with access network infrastructures. Access networks usually construct a Fog [2] infrastructure, which interacts with a Cloud environment, offering vehicular services. Many vehicular users can be served from each vehicle, while each user can simultaneously use multiple services with different constraints with each other. In this network environment, increased communication requirements are arising for the vehicular users and, thus, an increased number of network access resources is needed. To address this issue, the technology of Ultra-Dense Networking (UDN) [3, 4] indicates that dense 5G access network infrastructures can be implemented, where an increased number of Points of Access (PoAs) can provide plenty of communication resources to users [5, 6]. Regarding the operational principles of UDN, several different technologies can coexist in the network access environment. Indicatively, the Macrocells and Femtocells that construct the network access environment can implement technologies such as the IEEE 802.11p Wireless Access for Vehicular Environment (WAVE) [7], the 3 GPP Long-Term Evolution Advanced (LTE-A) [8], and the IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX) [9]. Furthermore, regarding the design principles of the 5G Core(5 GC) [10], the core network of a 5G vehicular architecture applies virtualization techniques for resource manipulation, including network slicing [11] and mobility management [12]. Communication among virtual network functionalities is achieved using RESTful application programming interfaces (RESTful APIs) [13] over HTTPS-/JSON-based protocols [14]. As a result, the implementation of novel network manipulation algorithms becomes independent of the specific underlying hardware optimizing the support for heterogeneous network technologies. The co-existence of heterogeneous wireless access technologies indicates the need for efficient manipulation of the vehicles' mobility. This need has involved the employment of the IEEE Media Independent Handover (MIH) and the Fast Proxy Mobile IPv6 (FPMIPv6) standards [15] for the mobility transfer. Specifically, the MIH protocol defines HO operations across different access networks. Its architecture provides generic interfaces and primitives independent of the type of vehicular equipment and access networks, supporting link-layer events and network information retrieval. On the other hand, two reference HO scenarios are introduced in FPMIPv6, the predictive and the reactive scenario, depending on the vehicle's speed. In the predictive scenario, the vehicle before disconnecting from its previous network sends a handover indication report to its Mobility Access Gateway (MAG) [16] declaring the new network, which is selected as the target for performing the HO. In case the vehicle connects to the new network without informing of its MAG in the previous network, the reactive operation of FPMIPv6 is performed. Vehicles should always be connected to the most appropriate access network to fulfil the QoS constraints of their services. To address this issue, Multi-Attribute Decision-Making (MADM) algorithms can be applied to select the best alternative among candidate networks by taking into consideration a set of evaluation criteria. Well-known methodologies include the Analytic Hierarchy Process (AHP) [17], the Analytic Network Process (ANP) [18], Simple Additive Weighting (SAW) [17; 19], Fuzzy SAW (FSAW) [20], Multiplicative Exponential Weighting (MEW) [17] and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [21]. This paper describes a methodology for supporting Handovers (HOs) on 5G vehicular networks. Both predictive and reactive HO modes are supported. During the vehicles movement, a velocity and alternative network monitoring process executes.