As the integration of unmanned aerial vehicles (UAVs) into visible light
communications (VLC) can offer many benefits for massive-connectivity
applications and services in 5G and beyond, this work considers a UAV-assisted
VLC using non-orthogonal multiple-access. More specifically, we formulate a
joint problem of power allocation and UAV's placement to maximize the sum rate
of all users, subject to constraints on power allocation, quality of service of
users, and UAV's position. Since the problem is non-convex and NP-hard in
general, it is difficult to be solved optimally. Moreover, the problem is not
easy to be solved by conventional approaches, e.g., coordinate descent
algorithms, due to channel modeling in VLC. Therefore, we propose using harris
hawks optimization (HHO) algorithm to solve the formulated problem and obtain
an efficient solution. We then use the HHO algorithm together with artificial
neural networks to propose a design which can be used in real-time applications
and avoid falling into the "local minima" trap in conventional trainers.
Numerical results are provided to verify the effectiveness of the proposed
algorithm and further demonstrate that the proposed algorithm/HHO trainer is
superior to several alternative schemes and existing metaheuristic algorithms.

本文提出了一种利用无人机的可见光通信辅助非正交多址接入的方法，通过使用 Harris 鹰优化算法和人工神经网络，实现了实时应用并避免了落入传统训练器的 “局部最小值” 陷阱，证明了该算法 / HHO 训练器优于其他方案和现有元启发式算法。