author = {S. Gracla and A. Schr\"{o}der and M. R\"{o}per and C. Bockelmann and D. W\"{u}bben and A. Dekorsy},
  year = {2023},
  month = {Jun},
  title = {Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications},
  URL = {https://2023.ieeeicassp.org/satellite-workshops/},
  address={Rhodos, Greece},
  abstract={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.},
  booktitle={SDPNGS 2023: Signal and Data Processing for Next Generation Satellites at 2023 IEEE International Conference on Acoustics, Speech and Signal Processing}