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 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). REFERENCES [1] M. Giordani and M. Zorzi, “Satellite communication at millimeter waves: A key enabler of the 6g era,” in 2020 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2020, pp. 383–388. [2] A. Cornejo, S. Landeros-Ayala, J. M. Matias, F. Ortiz-Gomez, R. Martinez, and M. Salas-Natera, “Method of rain attenuation prediction based on long–short term memory network,” Neural Processing Letters, vol. 54, no. 4, pp. 2959–2995, 2022. [3] P. Angeletti and R. De Gaudenzi, “Heuristic radio resource management for massive mimo in satellite broadband communication networks,” IEEE Access, vol. 9, pp. 147 164–147 190, 2021. [4] H.-K. Lim, J.-B. Kim, K. Kim, Y.-G. Hong, and Y.-H. Han, “Payloadbased traffic classification using multi-layer lstm in software defined networks,” Applied Sciences, vol. 9, no. 12, p. 2550, 2019. [5] Z. Wang, M. Lin, S. Sun, M. Cheng, and W.-P. Zhu, “Robust beamforming for enhancing user fairness in multibeam satellite systems with noma,” IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1010–1014, 2022. [6] P. J. Honnaiah, N. Maturo, S. Chatzinotas, S. Kisseleff, and J. Krause, “Demand-based adaptive multi-beam pattern and footprint planning for high throughput geo satellite systems,” IEEE Open Journal of the Communications Society, vol. 2, pp. 1526–1540, 2021. [7] I. T. Cummings, T. J. Schulz, T. C. Havens, and J. P. Doane, “Neural networks for real-time adaptive beamforming in simultaneous transmit and receive digital phased arrays: Student submission,” in 2019 IEEE International Symposium on Phased Array System & Technology (PAST), 2019, pp. 1–8. [8] J. A. Vasquez-Peralvo, J. Querol, F. Ort ´ ´ız, J. L. G. Rios, E. Lagunas, V. M. Baeza, G. Fontanesi, L. M. Garces-Socorr ´ as, J. C. M. Duncan, ´ and S. Chatzinotas, “Flexible beamforming for direct radiating arrays in satellite communications,” IEEE Access, 2023. [9] “Fnr smartspace project datasets,” accessed on October 13, 2023. [Online]. Available: https://fnr-smartspace-project.uni.lu/datasets/ [10] F. G. Ortiz-Gomez, D. Tarchi, R. Mart ´ ´ınez, A. Vanelli-Coralli, M. A. Salas-Natera, and S. Landeros-Ayala, “Supervised machine learning for power and bandwidth management in very high throughput satellite systems,” International Journal of Satellite Communications and Networking, vol. 40, no. 6, pp. 392–407, 2022. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/sat.1422

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

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