论文标题
在3D无人机蜂窝网络中服务任意人群的覆盖范围重叠问题
The Coverage Overlapping Problem of Serving Arbitrary Crowds in 3D Drone Cellular Networks
论文作者
论文摘要
为闪光灯提供覆盖范围是无人机基站（DBSS）的重要应用。但是，任何任意人群都可能以高密度分配。在每个DBS提供相同数量的地面用户的条件下，可以将多个DBS放置在相同的水平位置，但高度不同，并会引起严重的二流干扰，我们将其称为覆盖范围重叠问题。为了解决这个问题，我们提出了数据驱动的3D位置（DDP）和增强的DDP（EDDP）算法。提议的DDP和EDDP可以有效地找到在多项式时间内服务区域中DBS的适当数量，高度，位置和覆盖范围，以最大化系统总和率并保证用户设备的最低数据速率要求。仿真结果表明，与平衡的K-均值方法相比，提出的EDDP可以将系统总和率提高200％，并将计算时间减少50％。特别是，EDDP可以有效地减少覆盖范围重叠问题的发生，然后在系统总和率方面优于DDP约100％。
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.
