面向两层可见光通信网络的用户关联与无人机位置联合优化
Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks
AI总结：
针对两层无人机辅助VLC网络，提出联合用户关联与无人机部署的迭代优化算法，通过最小包围圆盘定位和无监督聚类，在满足照明与簇规模约束下提升接收数据量。
摘要：
本文研究了一种由无人机（UAV）辅助的可见光通信（VLC）系统，该系统包含两个层级：无人机到质心层和设备到设备（D2D）层。在无人机到质心层中，每架无人机可通过VLC链路同时为地面用户的质心提供通信和照明。在D2D层中，各质心通过D2D链路将从无人机接收的数据转发给簇成员。针对该网络，构建了用户关联与无人机部署位置的联合优化问题，目标是最大化接收数据量，同时满足照明约束和用户簇规模要求。文中首先提出一种迭代算法，将优化问题转化为一系列两个相互依赖的子问题。依据最小包围圆盘定理，设计了一种随机增量构造方法以寻找最优无人机位置；随后受无监督学习方法启发，提出一种聚类算法以寻找次优用户关联。仿真结果表明，所提方案平均使用户亮度比其阈值要求高出0.77 lux。此外，与采用射频链路替代VLC链路且不优化无人机位置的情形相比，所提方法下的接收比特率与D2D连接用户数之和提高了50.69%。
英文摘要：
In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light communication (VLC) has been considered which has two tiers: UAV-to-centroid and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can simultaneously provide communications and illumination for the centroids of the ground users over VLC links. In the D2D tier, the centroids retransmit received data from UAV over D2D links to the cluster members. For network, the optimization problem of joint user association and deployment location of UAVs is formulated to maximize the received data, satisfy illumination constraint, and the user cluster size. An iterative algorithm is first proposed to transform the optimization problem into a series of two interdependent sub problems. Following the smallest enclosing disk theorem, a random incremental construction method is designed to find the optimal UAV locations. Then, inspired by unsupervised learning method, a clustering algorithm to find a suboptimal user association is proposed. Our simulation results show that the proposed scheme on average guarantees the users brightness 0.77 lux more than their threshold requirements. Moreover, the received bitrate plus number of D2D connected users under our proposed method is 50.69% more than the scenario in which we have RF Link instead of VLC link and do not optimize UAV location.
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。
