Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications
This addresses the need for accurate robust precoding in Low Earth Orbit satellite-to-handheld links, which is an incremental improvement over existing simplified models.
The paper tackles the problem of robust precoding for cooperative multibeam satellite communications under imperfect channel state information, using a model-free deep reinforcement learning approach to learn robust precoding without knowledge of system imperfections.
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.