  Direct Low Earth Orbit satellite-to-handheld links are expected to be part of
a new era in satellite communications. Space-Division Multiple Access precoding
is a technique that reduces interference among satellite beams, therefore
increasing spectral efficiency by allowing cooperating satellites to reuse
frequency. Over the past decades, optimal precoding solutions with perfect
channel state information have been proposed for several scenarios, whereas
robust precoding with only imperfect channel state information has been mostly
studied for simplified models. In particular, for Low Earth Orbit satellite
applications such simplified models might not be accurate. In this paper, we
use the function approximation capabilities of the Soft Actor-Critic deep
Reinforcement Learning algorithm to learn robust precoding with no knowledge of
the system imperfections.
