  This paper centers around a multiple-input-multiple-output (MIMO) visible
light communication (VLC) system, where an unmanned aerial vehicle (UAV)
benefits from a light emitting diode (LED) array to serve photo-diode
(PD)-equipped users for illumination and communication simultaneously.
Concerning the battery limitation of the UAV and considerable energy
consumption of the LED array, a hybrid dimming control scheme is devised at the
UAV that effectively controls the number of glared LEDs and thereby mitigates
the overall energy consumption. To assess the performance of this system, a
radio resource allocation problem is accordingly formulated for jointly
optimizing the motion trajectory, transmit beamforming and LED selection at the
UAV, assuming that channel state information (CSI) is partially available. By
reformulating the optimization problem in Markov decision process (MDP) form,
we propose a soft actor-critic (SAC) mechanism that captures the dynamics of
the problem and optimizes its parameters. Additionally, regarding the frequent
mobility of the UAV and thus remarkable rearrangement of the system, we enhance
the trained SAC model by integrating a meta-learning strategy that enables more
adaptation to system variations. According to simulations, upgrading a
single-LED UAV by an array of 10 LEDs, exhibits 47% and 34% improvements in
data rate and energy efficiency, albeit at the expense of 8% more power
consumption.
