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Coverage Optimization with a Dynamic Network of Drone Relays

机译:用动态网络覆盖优化无人机继电器

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The integration of aerial base stations carried by drones in cellular networks offers promising opportunities to enhance the connectivity enjoyed by ground users. In this paper, we propose an optimization framework for the 3-D placement and repositioning of a fleet of drones with a realistic inter-drone interference model and drone connectivity constraints. We show how to maximize network coverage by means of an extremal-optimization algorithm. The design of our algorithm is based on a mixed-integer non-convex program formulation for a coverage problem that is NP-Complete, as we prove in the paper. We not only optimize drone positions in a 3-D space in polynomial time, but also assign flight routes solving an assignment problem and using a strong geometrical tool, namely Bezier curves, which are extremely useful for non-uniform and realistic topologies. Specifically, we propose to fly drones following Bezier curves to seek the chance of approaching to clusters of ground users. This enhances coverage over time while users and drones move. We assess the performance of our proposal for synthetic scenarios as well as realistic maps extracted from the topology of a capital city. We demonstrate that our framework is near-optimal and using Bezier curves increases coverage up to 47 percent while drones move.
机译:蜂窝网络中无人机携带的空中基站的整合提供了有希望的机会,可以增强地面用户享受的连接。在本文中,我们提出了一种优化框架,用于3-D放置和重新定位一种具有逼真的无人机间干扰模型和无人机连接约束。我们展示了如何通过极值优化算法最大限度地提高网络覆盖范围。我们的算法的设计基于混合整数非凸面编程配方,用于覆盖问题,因为我们在论文中证明了NP-Tress。我们不仅优化多项式时间的三维空间中的无人机位置,还可以分配求解分配问题的飞行路线,并使用强大的几何工具,即Bezier曲线,这对于非均匀和逼真的拓扑非常有用。具体而言,我们建议在Bezier曲线之后飞行无人机,以寻求接近地面用户的集群的机会。随着用户和无人机移动,这会随着时间的推移而增强覆盖范围。我们评估我们对综合情景的提案以及从首都拓扑中提取的现实地图的表现。我们证明,我们的框架近乎最佳,并且使用Bezier曲线在无人机移动时增加了高达47%的覆盖率。

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