This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Flor Ortiz, University of Luxembour;
(2) Juan A. Vasquez-Peralvo, University of Luxembour;
(3) Jorge Querol, University of Luxembour;
(4) Eva Lagunas, University of Luxembour;
(5) Jorge L. Gonzalez Rios, University of Luxembour;
(6) Marcele O. K. Mendonc¸a, University of Luxembour;
(7) Luis Garces, University of Luxembour;
(8) Victor Monzon Baeza, University of Luxembour;
(9) Symeon Chatzinotas, University of Luxembou.
Table of Links
- Abstract & Introduction
- System Model and Problem Statement
- Antenna Design and Training Data Generation
- Supervised Learning for Adaptive Beamforming
- Numerical Results
- Conclusions, Acknowledgment, and References
V. NUMERICAL RESULTS
The neural networks are trained offline, so they are used only for onboard satellite inference which drastically decreases the execution time, as explained in [10]. Both approaches are suitable for real-time beamforming adaptation due to the speed
where Precision measures the model’s ability not to misclassify negative instances, and Recall measures the model’s ability to find all positive instances.
Figure 2 shows the average KPIs obtained for the three approaches: Genetic Algorithm, Multi-Label Classification Neural Network (Approach 1), and Clustering and Classification Neural Network (Approach 2) after running and testing the three approaches iteratively with a sample size greater than 50,000 samples.
The Genetic Algorithm outperforms almost all the evaluated KPIs. However, the time required for execution and obtaining the beamforming matrix makes this algorithm unsuitable for real-time applications. On the other hand, both supervised learning-based approaches significantly reduce the execution time by more than a thousand times. This is because after training the ML models, they can be used for inference with almost immediate response times, making them suitable for real-time adaptation. Additionally, both ML-based approaches consistently maintain system performance above 90
Regarding classification metrics, Approach 2 outperforms Approach 1 because binary classification is typically less complex than multi-label classification. However, in terms of overall system performance, Approach 1 performs better than Approach 2.
