On the positioning likelihood of UAVs in 5G networks

An increment in the number of User Equipment (UE) demands network replanning or introducing incipient devices which can provide dynamic support to the subsisting networks. One of these devices can be the Unmanned Aerial Vehicles (UAVs). However, being prodigiously dynamic and autonomous in some scenarios, these vehicles require an efficient mechanism for their deployment in currently operating wireless networks. In this paper, an efficient approach is proposed which utilizes the properties of the self-healing neural model and the concept of matrix-coloring in order to maximize the UAVs positioning likelihood for optimized throughput coverage and maximum UE to UAV mapping. The efficacy of the proposed approach is demonstrated in terms of amelioration in the throughput coverage and mapping of the UAV to subdivisions at low consumption of energy and memory by using numerical simulations.