Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Abstract

The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

Availability of UAV Fleet Evaluation Based on Multi-State System

“…Studies [13], [19] - [22], [26] take into account the possibility of mission implementation by the UAV fleet, but the fleet is not the subject of investigations and mission success is evaluated as the first, and the fleet's reliability and its factors are not examined. The reliability of a UAV fleet is influenced by many factors such as its structure or topology [16], [27], type of redundancy [13], [28], drone characteristics [29], heterogeneity [30], and others. The taxonomy in reliability analysis of UAV fleets has been proposed in [9] that shows the important factors of this analysis and one of them is the development of the mathematical model of UAV fleet.…”

“…In this step, the mathematical model can be defined as BSS, for the evaluation of two performance levels, or MSS, for the analysis of more than two performance levels. Most studies of UAV fleet reliability and its special aspects are based on BSS [3], [4], [6], [13] - [16], [28], [30]. The topological aspects of UAV fleets closely correlate with reliability and are taken into account in fleet reliability.…”

Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System

“…where G d,u t is large-scale fading or path loss and Ω d,u t is small-scale fading [4]. The largescale fading coefficient can be written as reflected:…”

“…UAV-aided wireless communication systems are a promising component for future communication systems and could support a wide variety of applications with orders of magnitude more capacity than current systems [3]. In the future, UAVs will play a critical role in high-speed wireless communications, among many other applications enabled by UAVs [4]. By deploying UAVs with wireless communication capabilities, it will be possible to establish temporary wireless networks in areas where infrastructure is limited or damaged.…”

“…By deploying UAVs with wireless communication capabilities, it will be possible to establish temporary wireless networks in areas where infrastructure is limited or damaged. This flexibility could also be useful in emergencies, such as natural disasters or search and rescue operations, in which the communication infrastructure is disrupted [4].…”