Optimizing beam selection and resource allocation in UAV-aided vehicular networks

Future mobile radio networks require a degree of flexibility that technologies like Unmanned Aerial Vehicles (UAVs) carrying Base Stations (BSs) can provide. It is expected that the lower space above cities will be populated by many different types of UAVs, such as taxis and smaller drones used for logistics or patrolling, which can be equipped with BSs to serve users on the ground, while flying for their given mission. We investigate an urban scenario with terrestrial macro BSs (MBSs) deployed, where multiple UAVs are flying on a given path. Vehicles in the area are moving while relying on network services, and MBSs alone might not serve them adequately. UAVs operate as BSs, helping the MBSs. Vehicles are assumed to be satisfied if an appropriate quality of experience (QoE) is fulfilled, that is they are able to upload a given amount of data during a given time window, continuously. We assume BSs use beamforming and a limited number of beams can be activated at the same time on UAVs. This paper proposes an optimization algorithm allowing to select the best set of beams to be activated at each UAV and the best set of resource units per vehicle, in order to maximaze the QoE. The algorithm jointly considers: i) resource management at both MBSs and UAVs; ii) traffic prioritization to attain the continuous service; iii) a limited backhaul capacity. Numerical results show the notable improvement of satisfied users when the flying BSs are present and report the impact of backhaul capacity.