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On the positioning likelihood of UAVs in 5G networks

机译:关于无人机在5G网络中的定位可能性

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摘要

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. (C) 2018 Published by Elsevier B.V.
机译:用户设备(UE)数量的增加要求对网络进行重新规划或引入可以为现有网络提供动态支持的初始设备。这些设备之一可以是无人驾驶飞机(UAV)。但是,在某些情况下,这些车辆具有极大的动态性和自治性,因此需要一种有效的机制将其部署在当前运行的无线网络中。在本文中,提出了一种有效的方法,该方法利用自愈神经模型的属性和矩阵着色的概念来最大化UAV的定位可能性,以优化吞吐量覆盖范围并最大程度地实现UE至UAV的映射。通过使用数值模拟,通过改善吞吐量覆盖范围以及将无人机以较低的能量和内存消耗映射到细分,可以证明所提出方法的有效性。 (C)2018由Elsevier B.V.发布

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