在3D无人机蜂窝网络中为任意人群提供覆盖的重叠问题
The Coverage Overlapping Problem of Serving Arbitrary Crowds in 3D Drone Cellular Networks
AI总结：
本文提出eDDP算法，通过数据驱动方法优化3D无人机基站部署，有效解决覆盖重叠问题，提升系统总速率
摘要：
为闪 crowds 提供覆盖是无人机基站（DBSs）的重要应用。然而，任何任意人群都可能以高密度分布。在每个DBS服务于相同数量的地面用户的情况下，多个DBS可能被放置在相同的水平位置但不同的高度，从而导致严重的同频干扰，我们称之为覆盖重叠问题。为了解决这个问题，我们提出了数据驱动的3D放置（DDP）和增强的DDP（eDDP）算法。所提出的DDP和eDDP可以在多项式时间内有效找到适当数量、高度、位置和覆盖范围的DBSs，在服务区域中最大化系统总速率并保证用户设备的最小数据速率要求。仿真结果表明，与平衡k均值方法相比，所提出的eDDP可以将系统总速率提高200%，并将计算时间减少50%。特别是，eDDP可以有效减少覆盖重叠问题的发生，从而在系统总速率方面比DDP高出约100%。
英文摘要：
Providing coverage for flash crowds is an important application for drone base stations (DBSs). However, any arbitrary crowd is likely to be distributed at a high density. Under the condition for each DBS to serve the same number of ground users, multiple DBSs may be placed at the same horizontal location but different altitudes and will cause severe co-channel interference, to which we refer as the coverage overlapping problem. To solve this problem, we then proposed the data-driven 3D placement (DDP) and the enhanced DDP (eDDP) algorithms. The proposed DDP and eDDP can effectively find the appropriate number, altitude, location, and coverage of DBSs in the serving area in polynomial time to maximize the system sum rate and guarantee the minimum data rate requirement of the user equipment. The simulation results show that, compared with the balanced k-means approach, the proposed eDDP can increase the system sum rate by 200% and reduce the computation time by 50%. In particular, eDDP can effectively reduce the occurrence of the coverage overlapping problem and then outperform DDP by about 100% in terms of system sum rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
