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Supervised Learning Based Real-Time Adaptive Beamforming: Conclusions, Acknowledgment, & References

by Transcompiler: Learn How to Translate CodeFebruary 23rd, 2024

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While software-defined payloads are very promising, their effective utilization requires advanced RRM techniques to optimize resource allocation in real-time.

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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.

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VI. CONCLUSIONS

We present two novel approaches to adaptive beamforming in satellite communications systems and compare them to the traditional GA. Our goal was to explore more efficient and real-time alternatives to GA, which, although effective, can be computationally expensive. Both approaches offer viable alternatives to GA for adaptive beamforming in satellite communication systems. These approaches not only reduce execution times but also maintain high system performance.

Selection between the two approaches should be based on the specific requirements of the application, taking into account the trade-off between classification complexity and overall system performance. As a result, these findings open up new possibilities for implementing efficient, real-time adaptive beamforming in wireless communication systems.

Future lines of research may include further optimizations of these approaches, exploration of hybrid models, or incorporation of additional features to improve performance and versatility.

ACKNOWLEDGMENT

This work was supported by the European Space Agency (ESA) funded under Contract No. 4000134522/21/NL/FGL named “Satellite Signal Processing Techniques using a Commercial Off-The-Shelf AI Chipset (SPAICE)”. Please note that the views of the authors of this paper do not necessarily reflect the views of the ESA. Furthermore, this work was partially supported by the Luxembourg National Research Fund (FNR) under the project SmartSpace (C21/IS/16193290).

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