Managing Sets of Flying Base Stations Using Energy Efficient 3D Trajectory Planning in Cellular Networks

Unmanned aerial vehicles (UAVs) in cellular networks have garnered considerable interest. One of their applications is as flying base stations (FBSs), which can increase coverage and quality of service (QoS). Because FBSs are battery-powered, regulating their energy usage is a vital aspect of their use; therefore, the appropriate placement and trajectories of FBSs throughout their operation are critical to overcoming this challenge. In this article, we propose a method of solving a multi-FBS 3D trajectory problem that considers FBS energy consumption, operation time, flight distance limits, and intercell interference constraints. Our method is divided into two phases: FBS placement and FBS trajectory. In taking this approach, we break the problem into several snapshots. First, we find the minimum number of FBSs required and their proper 3D positions in each snapshot. Then, between every two snapshots, the trajectory phase is executed. The optimal path between the origin and destination of each FEB is determined during the trajectory phase by utilizing a proposed binary linear problem (BLP) model that considers FBS energy consumption and flight distance constraints. Then, the shortest path for each FBS is determined while taking obstacles and collision avoidance into consideration. The number of FBSs needed may vary between snapshots, so we present an FBS set management (FSM) technique to manage the set of FBSs and their power. The results demonstrate that the proposed approach is applicable to real-world situations and that the outcomes are consistent with expectations.

Automated summary

In this article, a method for solving the multi-FBS 3D trajectory problem is proposed, taking into account FBS energy consumption, operation time, flight distance limits, and intercell interference constraints. The method is divided into two phases: FBS placement and FBS trajectory. The problem is broken into several snapshots, where the minimum number of FBSs and their positions are determined. The trajectory phase determines the optimal path for each FBS, considering energy consumption, flight distance, obstacles, and collision avoidance. The number of FBSs needed may vary between snapshots, and an FSM technique is presented to manage the FBS set and their power.