  Optical wireless communication (OWC) provides high aggregate data rates in
the range of Terabits per second (Tb/s). Specifically, OWC using infrared
lasers as transmitters has been considered as a strong candidate in the next
generation of wireless communication. Rate splitting (RS) is a transmission
scheme derived to improve spectral efficiency in dense wireless networks. In
RS, the transmitted power is allocated to different messages, common and
private messages, serving multiple users simultaneously, where each user can
decode the desired message following a certain procedure. Moreover, two-tier
precoding RS scheme has been proposed to overcome the limitations of
traditional RS in multi-group scenarios. In this context, power allocation (PA)
is a crucial issue, which can affect the performance of RS. Therefore, we
formulate a PA optimization problem to enhance the data rates of RS-based OWC
networks. However, such optimization problems are complex due to the use of
different messages intended to the users. In this paper, we design and train a
deep neural network (DNN) model to determine the power allocated to the
messages of RS, while fulfilling the demands of users. The results show the
accuracy of our trained DNN model when used in an online phase.
