Model-Free Robust Beamforming in Satellite Downlink using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based approach for robust satellite downlink beamforming that adapts to imperfect channel information, outperforming traditional methods across various scenarios.

Contribution

It develops a novel reinforcement learning algorithm for robust satellite beamforming, addressing the intractability of analytical solutions under imperfect channel knowledge.

Findings

RL-based precoding outperforms analytical baselines in sum rate.

The algorithm adapts effectively to different uncertainty levels.

Public implementation is provided for reproducibility.

Abstract

Satellite-based communications are expected to be a substantial future market in 6G networks. As satellite constellations grow denser and transmission resources remain limited, frequency reuse plays an increasingly important role in managing inter-user interference. In the multi-user downlink, precoding enables the reuse of frequencies across spatially separated users, greatly improving spectral efficiency. The analytical calculation of suitable precodings for perfect channel information is well studied, however, their performance can quickly deteriorate when faced with, e.g., outdated channel state information or, as is particularly relevant for satellite channels, when position estimates are erroneous. Deriving robust precoders under imperfect channel state information is not only analytically intractable in general but often requires substantial relaxations of the optimization problem or heuristic constraints to obtain feasible solutions. Instead, in this paper we flexibly derive robust precoding algorithms from given data using reinforcement learning. We describe how we adapt the applied Soft Actor-Critic learning algorithm to the problem of downlink satellite beamforming and show numerically that the resulting precoding algorithm adjusts to all investigated scenarios. The considered scenarios cover both single satellite and cooperative multi-satellite beamforming, using either global or local channel state information, and two error models that represent increasing levels of uncertainty. We show that the learned algorithms match or markedly outperform two analytical baselines in sum rate performance, adapting to the required level of robustness. We also analyze the mechanisms that the learned algorithms leverage to achieve robustness. The implementation is publicly available for use and reproduction of the results.