In this paper, the problem of optimizing the deployment of unmanned aerial
vehicles (UAVs) equipped with visible light communication (VLC) capabilities is
studied. In the studied model, the UAVs can simultaneously provide
communications and illumination to service ground users. Ambient illumination
increases the interference over VLC links while reducing the illumination
threshold of the UAVs. Therefore, it is necessary to consider the illumination
distribution of the target area for UAV deployment optimization. This problem
is formulated as an optimization problem whose goal is to minimize the total
transmit power while meeting the illumination and communication requirements of
users. To solve this problem, an algorithm based on the machine learning
framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can
model the long-term historical illumination distribution and predict the future
illumination distribution. In order to reduce the complexity of the prediction
algorithm while accurately predicting the illumination distribution, a Gaussian
mixture model (GMM) is used to fit the illumination distribution of the target
area at each time slot. Based on the predicted illumination distribution, the
optimization problem is proved to be a convex optimization problem that can be
solved by using duality. Simulations using real data from the Earth
observations group (EOG) at NOAA/NCEI show that the proposed approach can
achieve up to 22.1% reduction in transmit power compared to a conventional
optimal UAV deployment that does not consider the illumination distribution.
The results also show that UAVs must hover at areas having strong illumination,
thus providing useful guidelines on the deployment of VLC-enabled UAVs.

该论文研究了无人机的部署优化问题，提出了一种基于深度学习模型的算法用于解决通信和照明要求下的传输功率最小化问题，在真实数据上测试表明与传统解法相比，最佳部署下可达到 22.1% 的功率降低并给出了部署和工作指导。