# Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications

arXiv: https://arxiv.org/abs/2303.11427 · HTML: https://mlsanity.com/paper/2303.11427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019)
- [Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness](https://mlsanity.com/paper/2006.16646/) (2020)
- [Data Augmentation Empowered Neural Precoding for Multiuser MIMO with MMSE Model](https://mlsanity.com/paper/2203.02196/) (2022)
- [Beamspace MIMO for Satellite Swarms](https://mlsanity.com/paper/2112.08791/) (2021)
- [Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission](https://mlsanity.com/paper/2301.12973/) (2023)

## Abstract (arXiv)

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.