Joint trajectory and transmission optimization for energy efficient UAV enabled eLAA network

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

“…UAVs are taking over important roles in society, especially in the field of telecommunications, where they are the main solution when temporary support and a relatively low cost-benefit ratio are required. Works such as [6,17] try to minimize the UAV battery cost, which increases efficiency and uptime. One of the most common solutions to this problem is to optimize the trajectory and find the optimal positioning of the UAV to increase energy efficiency.…”

“…Therefore, determining the traffic on the network is extremely important to allow for planning and to support the network at critical moments. For temporary events such as concerts and sporting events, it is easier to calculate and predict the instantaneous traffic than in situations where the mobile network load fluctuates and depends on time and space or due to an environmental disaster [6]. In this context, the use of unmanned aerial vehicles (UAVs) mounted on base stations (UAV-BS) can serve to complete the existing mobile network structure [7], provide wireless communication services to blocked users, increase the capacity of the mobile system [8], minimize coverage gaps, and help reduce the sudden increase in demand that is one of the main problems leading to congestion during hotspot events [9].…”

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

“…Existing literature [27], [34], [35] focuses on optimizing the problem with low user mobility. Nevertheless, the original considerations seem impractical when it comes to vehicle users, and another literature assumes the vehicles move along a straight line [13].…”