  This paper studies a deep learning (DL) framework for the design of binary
modulated visible light communication (VLC) transceiver with universal dimming
support. The dimming control for the optical binary signal boils down to a
combinatorial codebook design so that the average Hamming weight of binary
codewords matches with arbitrary dimming target. An unsupervised DL technique
is employed for obtaining a neural network to replace the encoder-decoder pair
that recovers the message from the optically transmitted signal. In such a
task, a novel stochastic binarization method is developed to generate the set
of binary codewords from continuous-valued neural network outputs. For
universal support of arbitrary dimming target, the DL-based VLC transceiver is
trained with multiple dimming constraints, which turns out to be a constrained
training optimization that is very challenging to handle with existing DL
methods. We develop a new training algorithm that addresses the dimming
constraints through a dual formulation of the optimization. Based on the
developed algorithm, the resulting VLC transceiver can be optimized via the
end-to-end training procedure. Numerical results verify that the proposed
codebook outperforms theoretically best constant weight codebooks under various
VLC setups.
