Trajectory Estimation Based Cell Selection for UAVAssisted Emergency Cellular Networks

Abstract

Unmanned Aerial Vehicles (UAVs) can be employed as aerial base stations (BSs) due to their high mobility and flexible deployment. UAV has the most promising future in com munication network increase in flight time and mobility to move in 3-D space make UAVs useful. Whenever the need for static or terrestrial base stations increases, We can deploy UAVs that serve as BS. Nowadays application of UAV is increased alarmingly. In UAV communication, cell selection defines the criteria or metric on which a UE selects the base station or UAV. This dissertation contains the work done to improve the user association scheme. The scheme uses the trajectory, velocity, and user count information for user as sociation. Our work reflects that we can not ignore the trajectory and velocity information while making user association decisions. This work also focuses on optimizing the trans mission power, so that system throughput improves significantly. We use the maximum average Signal-to-interference-plus-noise-ratio (SINR) for user association.