  In this paper, the problem of dynamical deployment of unmanned aerial
vehicles (UAVs) equipped with visible light communication (VLC) capabilities
for optimizing the energy efficiency of UAV-enabled networks is studied. In the
studied model, the UAVs can simultaneously provide communications and
illumination to service ground users. Since ambient illumination increases the
interference over VLC links while reducing the illumination threshold of the
UAVs, it is necessary to consider the illumination distribution of the target
area for UAV deployment optimization. This problem is formulated as an
optimization problem which jointly optimizes UAV deployment, user association,
and power efficiency while meeting the illumination and communication
requirements of users. To solve this problem, an algorithm that combines the
machine learning framework of gated recurrent units (GRUs) with convolutional
neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the
long-term historical illumination distribution and predict the future
illumination distribution. Given the prediction of illumination distribution,
the original nonconvex optimization problem can be divided into two
sub-problems and is then solved using a low-complexity, iterative algorithm.
Then, the proposed algorithm enables UAVs to determine the their deployment and
user association to minimize the total transmit power. Simulation results using
real data from the Earth observations group (EOG) at NOAA/NCEI show that the
proposed approach can achieve up to 68.9% reduction in total transmit power
compared to a conventional optimal UAV deployment that does not consider the
illumination distribution and user association.
