Smart Unmanned Aerial Vehicles as base stations placement to improve the mobile network operations

Abstract Future mobile communication networks need Unmanned Aerial Vehicles as Base Stations (UAVasBSs) with the fast-moving and long-term hovering capabilities to guarantee consistent network performance. UAVasBSs help 5G/B5G mobile communication systems to rapidly recover from emergency situations and handle the instant traffic of the flash crowd. In this context, multiple UAVs might form a flying ad-hoc network to establish a flying access network to enhance the network connectivity and service quality. Therefore, it is important to determine the optimal number and locations of UAVasBSs in a fast and efficient way to cover the target area to provide temporary yet reliable cellular connectivity. The use of Artificial Intelligence (AI) and network data analysis are key tools to fulfill the above issues. In this article, we propose a smart UAVasBS placement (SUAP) mechanism to improve the mobile network operations in flash crowd and emergency situations. We have modeled such an UAVasBS placement task as an optimization problem to obtain required network connectivity and system performance, and resolved it with a genetic algorithm using the network context information. Simulation results show that our proposal could cover 90% of mobile users, and it provides nearly 90% packet delivery ratio for users with a fast convergence rate.