# QIMO: Q-Learning-Based Adaptive Impairment Margin Optimization in DVB-S2X Satellite Communication

## TL;DR

This paper introduces QIMO, a Q-learning-based method for optimizing impairment margins in satellite communication to improve efficiency and reduce errors.

## Contribution

The novelty is a low-complexity Q-learning algorithm for adaptive impairment margin optimization in DVB-S2X satellite systems.

## Key findings

- QIMO achieves higher average spectrum efficiency compared to expert and default methods.
- The method reduces low-efficiency test cases and increases high-efficiency cases.
- Passive exploration with fill frames enables error-free optimization.

## Abstract

Adaptive coding and modulation (ACM) is a key feature in satellite broadcasting; it allows the dynamic selection of modulation and coding (MODCOD) schemes based on channel conditions. The selection is based on the quasi-error-free (QEF) threshold and additional margins. We introduce three distinct types of margins for improved robustness. One of these margins, impairment margin (IM), depends on the nonlinearities of different components in the satellite channel. Current IM selection methods require expert intervention; are costly and prone to errors; and only allow a discrete set of environments. We aim to develop a low-complexity algorithm that converges fast and is quasi-error-free on user traffic due to a non-intrusive exploration method. For this, we propose a Q-learning-based solution that uses passive exploration, with fill frames, to allow error-free IM optimization. Our solution shows a higher average spectrum efficiency compared to expert and default IMs, with fewer low efficiency test cases and more high-efficiency cases.