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

Area of Science:

• Satellite Communications
• Signal Processing
• Machine Learning

Background:

• Adaptive coding and modulation (ACM) is crucial for satellite broadcasting, dynamically adjusting modulation and coding (MODCOD) schemes based on channel conditions.
• Current impairment margin (IM) selection for ACM is complex, requiring expert input, and is prone to errors and limitations in environmental adaptability.
• Optimizing IM is essential for robust satellite channel performance, balancing efficiency with error-free operation.

Purpose of the Study:

• To develop a low-complexity, fast-converging algorithm for quasi-error-free (QEF) impairment margin (IM) optimization in satellite ACM.
• To enable non-intrusive IM optimization that does not affect user traffic during the exploration phase.
• To improve spectrum efficiency compared to existing IM selection methods.

Main Methods:

• Proposed a Q-learning-based algorithm utilizing passive exploration with fill frames for IM optimization.
• The algorithm aims for quasi-error-free (QEF) operation on user traffic by employing non-intrusive exploration techniques.
• Evaluated the algorithm's performance against expert-defined and default IM selection methods.

Main Results:

• The proposed Q-learning solution demonstrated higher average spectrum efficiency compared to expert and default IMs.
• Observed fewer instances of low spectrum efficiency and a greater number of high-efficiency cases with the new algorithm.
• The passive exploration method allowed for error-free optimization of impairment margins.

Conclusions:

• A novel Q-learning algorithm provides an efficient and automated method for optimizing impairment margins in satellite ACM.
• The approach significantly enhances spectrum efficiency while maintaining quasi-error-free performance.
• This automated, low-complexity solution overcomes the limitations of manual IM selection methods.