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Intelligent Space Communication Networks

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A Roadmap to Future Space Connectivity

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Nowadays more than ever, we are witnessing an astonishing design and development of Artificial Intelligence (AI)-based solutions applied to a very wide set of problems and systems. Even if some of these techniques were already known, the world technological level was not high enough to guarantee their useful development in a wide set of applications and services. This recently changed. AI-based solutions are currently under study and development in communication networks to make them more “aware” of their situation, help them improve some of their functionalities (such as resource allocation), and allow them to offer an improved and more “smart” service to the final users. This applies also to satellite communication networks. This chapter offers an overview of the improvements due to the introduction of AI-based solutions in satellite communication networks, the aspects that distinguish the different solutions, the functionalities offered thanks to this integration, and the status of the main research activities and projects in this research field.

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References

  1. F. Fourati, M.-S. Alouini, Artificial intelligence for satellite communication: a review. Intell. Converged Netw. 2(3), 213–243 (2021)

    Article  Google Scholar 

  2. L. Ziluan, L. Xin, Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks. J. Chin. Univ. Posts Telecommun. 25(1), 15–28 (2018)

    Google Scholar 

  3. Z. Na, Z. Pan, X. Liu, Z. Deng, Z. Gao, Q. Guo, Distributed routing strategy based on machine learning for LEO satellite network. Hindawi Wirel. Commun. Mobile Comput. 2018 (2018)

    Google Scholar 

  4. Y. Bie, L. Wang, Y. Tian, Z. Hu, A combined forecasting model for satellite network self-similar traffic. IEEE Access 7, 152004–152013 (2019)

    Article  Google Scholar 

  5. E. Ostlin, H.-J. Zepernick, H. Suzuki, Macrocell path-loss prediction using artificial neural networks. IEEE Trans. Veh. Technol. 59(6), 2735–2747 (2010)

    Article  Google Scholar 

  6. J. Thrane, D. Zibar, H.L. Christiansen, Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz. IEEE Access 8, 7925–7936 (2020)

    Google Scholar 

  7. B.A. Homssi, K. Dakic, K. Wang, T. Alpcan, B. Allen, S. Kandeepan, A. Al-Hourani, W. Saad, Artificial intelligence techniques for next-generation mega satellite networks (2022). Preprint. arXiv:2207.00414

    Google Scholar 

  8. Y. Yuan, Z. Sun, Z. Wei, K. Jia, DeepMorse: a deep convolutional learning method for blind morse signal detection in wideband wireless spectrum. IEEE Access 7, 80577–80587 (2019)

    Article  Google Scholar 

  9. H. Huang, J.-Q. Li, J. Wang, H. Wang, FCN-based carrier signal detection in broadband power spectrum. IEEE Access 8, 113042–113051 (2020)

    Article  Google Scholar 

  10. C. Politis, S. Maleki, C. Tsinos, S. Chatzinotas, B. Ottersten, On-board the satellite interference detection with imperfect signal cancellation. IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2016), pp. 1–5

    Google Scholar 

  11. Q. Liu, J. Yang, C. Zhuang, A. Barnawi, B.A. Alzahrani, Artificial intelligence based mobile tracking and antenna pointing in satellite-terrestrial network. IEEE Access 7, 177497–177503 (2019)

    Article  Google Scholar 

  12. L. Pellaco, N. Singh, J. Jaldén, Spectrum prediction and interference detection for satellite communications, in IET International Communications Satellite Systems Conference (2019), pp. 1–8

    Google Scholar 

  13. P. Henarejos, M.A. Vázquez, A.I. Pérez-Neira, Deep learning for experimental hybrid terrestrial and satellite interference management. IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2019), pp. 1–5

    Google Scholar 

  14. X. Hu, S. Liu, Y. Wang, L. Xu, Y. Zhang, C. Wang, W. Wang, Deep reinforcement learning-based beam Hopping algorithm in multibeam satellite systems. Wiley IET Commun. 13(16), 2485–2491 (2019)

    Article  Google Scholar 

  15. L. Lei, E. Lagunas, Y. Yuan, M.G. Kibria, S. Chatzinotas, B. Ottersten, Beam illumination pattern design in satellite networks: learning and optimization for efficient beam hopping. IEEE Acccess 8, 136655–136667 (2020)

    Article  Google Scholar 

  16. X. Hu, L. Wang, Y. Wang, S. Xu, Z. Liu, W. Wang, Dynamic beam hopping for DVB-S2X GEO satellite: a DRL-powered GA approach. IEEE Commun. Lett. 26(4), 808–812 (2022)

    Article  Google Scholar 

  17. V. Kothari, E. Liberis, n.d. Lane, The final frontier: Deep learning in space, in 21st International Workshop on Mobile Computing Systems and Applications (2020), pp. 45–49

    Google Scholar 

  18. H. Tsuchida, Y. Kawamoto, N. Kato, K. Kaneko, S. Tani, S. Uchida, H. Aruga, Efficient power control for satellite-borne batteries using Q-learning in low-earth-orbit satellite constellations. IEEE Wirel. Commun. Lett. 9(6), 809–812 (2020)

    Article  Google Scholar 

  19. B. Zhao, J. Liu, Z. Wei, I. You, A deep reinforcement learning based approach for energy-efficient channel allocation in satellite internet of things. IEEE Access 8, 6219–62206 (2020)

    Google Scholar 

  20. C. Han, Y. Niu, Cross-layer anti-jamming scheme: a hierarchical learning approach. IEEE Access 6, 34874–34883 (2018)

    Article  Google Scholar 

  21. F. Yao, L. Jia, Y. Sun, Y. Xu, S. Feng, Y. Zhu, A hierarchical learning approach to anti-jamming channel selection strategies. Springer Wirel. Netw. 25(1), 201–213 (2019)

    Article  Google Scholar 

  22. L. Xiao, D. Jiang, D. Xu, H. Zhu, Y. Zhang, H.V. Poor, Two-dimensional antijamming mobile communication based on reinforcement learning. IEEE Trans. Veh. Technol. 67(10), 9499–9512 (2018)

    Article  Google Scholar 

  23. C. Han, L. Huo, X. Tong, H. Wang, X. Liu, Spatial anti-jamming scheme for internet of satellites based on the deep reinforcement learning and Stackelberg game. IEEE Trans. Veh. Technol. 69(5), 5331–5342 (2020)

    Article  Google Scholar 

  24. T. Yairi, N. Takeishi, T. Oda, Y. Nakajima, N. Nishimura, N. Takata, A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Trans. Aerosp. Electron. Syst. 53(3), 1384–1401 (2017)

    Article  Google Scholar 

  25. S.K. Ibrahim, A. Ahmed, M.A. Zeidan, I.E. Ziedan, Machine learning methods for spacecraft telemetry mining. IEEE Trans. Aerosp. Electron. Syst. 55(4), 1816–1827 (2018)

    Article  Google Scholar 

  26. P. Wan, Y. Zhan, W. Jiang, Study on the satellite telemetry data classification based on self-learning. IEEE Access 8, 2656–2669 (2019)

    Article  Google Scholar 

  27. B. Zhao, J. Liu, Z. Wei, I. You, Orbital edge offloading on mega-LEO satellite constellations for equal access to computing. IEEE Commun. Mag. 60(4), 32–36 (2022)

    Article  Google Scholar 

  28. N. Cheng, F. Lyu, W. Quan, C. Zhou, H. He, W. Shi, X. Shen, Space/aerial-assisted computing offloading for IoT applications: a learning-based approach. IEEE J. Sel. Areas Commun. 37(5), 1117–1129 (2019)

    Article  Google Scholar 

  29. G. Cui, X. Li, L. Xu, W. Wang, Latency and energy optimization for MEC enhanced SAT-IoT networks. IEEE Access 8, 55915–55926 (2020)

    Article  Google Scholar 

  30. G. Furano, G. Meoni, A. Dunne, D. Moloney, V. Ferlet-Cavrois, A. Tavoularis, J. Byrne, L. Buckley, M. Psarakis, K.-O. Voss, Towards the use of artificial intelligence on the edge in space systems: Challenges and opportunities. IEEE Aerosp. Electron. Syst. Mag. 35(12), 44–56 (2020)

    Article  Google Scholar 

  31. A.S. Li, V. Chirayath, M. Segal-Rozenhaimer, J.L. Torres-Perez, J. van den Bergh, NASA NeMO-net’s convolutional neural network: mapping marine habitats with spectrally heterogeneous remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 5115–5133 (2020)

    Article  Google Scholar 

  32. G. Mateo-García, V. Laparra, D. López-Puigdollers, L. Gómez-Chova, Cross-sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 14, 747–761 (2020)

    Article  Google Scholar 

  33. F. Wang, F. Liao, H. Zhu, FPA-DNN: a forward propagation acceleration based deep neural network for ship detection, in IEEE International Joint Conference on Neural Networks (IJCNN) (2020), pp. 1–8

    Google Scholar 

  34. N. Kato, Z.M. Fadlullah, F. Tang, B. Mao, S. Tani, A. Okamura, J. Liu, Optimizing space-air-ground integrated networks by artificial intelligence. IEEE Wirel. Commun. 26(4), 140–147 (2019)

    Article  Google Scholar 

  35. J.-H. Lee, J. Park, M. Bennis, Y.-C. Ko, Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks, in IEEE Global Communications Conference (GLOBECOM) (2020), pp. 1–6

    Google Scholar 

  36. C. Jiang, X. Zhu, Reinforcement learning based capacity management in multi-layer satellite networks. IEEE Trans. Wirel. Commun. 19(7), 4685–4699 (2020)

    Article  Google Scholar 

  37. A. Russo, G. Lax, Using artificial intelligence for space challenges: A survey. Appl. Sci. 12.10, 5106, (2022)

    Article  Google Scholar 

  38. G. Labrèche, D. Evans, D. Marszk, T. Mladenov, V. Shiradhonkar, T. Soto, V, Zelenevskiy, OPS-SAT spacecraft autonomy with TensorFlow lite, unsupervised learning, and online machine learning. IEEE Aerospace Conference (AERO) (2022), pp. 1–17

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di ingegneria navale, elettrica, elettronica e delle telecomunicazioni (DITEN), University of Genova, Genova, Italy

    Mario Marchese & Fabio Patrone

  2. University of Florence, Florence, Italy

    Simone Morosi

Authors
  1. Mario Marchese
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  2. Simone Morosi
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  3. Fabio Patrone
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Corresponding author

Correspondence to Mario Marchese .

Editor information

Editors and Affiliations

  1. Department of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy

    Claudio Sacchi

  2. Department of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy

    Fabrizio Granelli

  3. Deutsche Telekom Chair of Communication Networks, Technische Universität Dresden, Dresden, Germany

    Riccardo Bassoli

  4. Deutsche Telekom Chair of Communication Networks, Technische Universität Dresden, Dresden, Germany

    Frank H. P. Fitzek

  5. Center for Teleinfrastructures (CTIF), University of Roma “Tor Vergata”, Roma, Italy

    Marina Ruggieri

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Marchese, M., Morosi, S., Patrone, F. (2023). Intelligent Space Communication Networks. In: Sacchi, C., Granelli, F., Bassoli, R., Fitzek, F.H.P., Ruggieri, M. (eds) A Roadmap to Future Space Connectivity. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-30762-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-30762-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30761-4

  • Online ISBN: 978-3-031-30762-1

  • eBook Packages: EngineeringEngineering (R0)

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