{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T04:49:45Z","timestamp":1749703785589,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031307614"},{"type":"electronic","value":"9783031307621"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30762-1_7","type":"book-chapter","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T16:01:40Z","timestamp":1689091300000},"page":"171-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent Space Communication Networks"],"prefix":"10.1007","author":[{"given":"Mario","family":"Marchese","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Morosi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio","family":"Patrone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"issue":"3","key":"7_CR1","doi-asserted-by":"publisher","first-page":"213","DOI":"10.23919\/ICN.2021.0015","volume":"2","author":"F Fourati","year":"2021","unstructured":"F. Fourati, M.-S. Alouini, Artificial intelligence for satellite communication: a review. Intell. Converged Netw. 2(3), 213\u2013243 (2021)","journal-title":"Intell. Converged Netw."},{"issue":"1","key":"7_CR2","first-page":"15","volume":"25","author":"L Ziluan","year":"2018","unstructured":"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\u201328 (2018)","journal-title":"J. Chin. Univ. Posts Telecommun."},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1155\/2018\/3026405"},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"152004","DOI":"10.1109\/ACCESS.2019.2944895","volume":"7","author":"Y Bie","year":"2019","unstructured":"Y. Bie, L. Wang, Y. Tian, Z. Hu, A combined forecasting model for satellite network self-similar traffic. IEEE Access 7, 152004\u2013152013 (2019)","journal-title":"IEEE Access"},{"issue":"6","key":"7_CR5","doi-asserted-by":"publisher","first-page":"2735","DOI":"10.1109\/TVT.2010.2050502","volume":"59","author":"E Ostlin","year":"2010","unstructured":"E. Ostlin, H.-J. Zepernick, H. Suzuki, Macrocell path-loss prediction using artificial neural networks. IEEE Trans. Veh. Technol. 59(6), 2735\u20132747 (2010)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"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\u20137936 (2020)","DOI":"10.1109\/ACCESS.2020.2964103"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.36227\/techrxiv.20073125.v1"},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"80577","DOI":"10.1109\/ACCESS.2019.2923084","volume":"7","author":"Y Yuan","year":"2019","unstructured":"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\u201380587 (2019)","journal-title":"IEEE Access"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"113042","DOI":"10.1109\/ACCESS.2020.3003683","volume":"8","author":"H Huang","year":"2020","unstructured":"H. Huang, J.-Q. Li, J. Wang, H. Wang, FCN-based carrier signal detection in broadband power spectrum. IEEE Access 8, 113042\u2013113051 (2020)","journal-title":"IEEE Access"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"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\u20135","DOI":"10.1109\/SPAWC.2016.7536813"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"177497","DOI":"10.1109\/ACCESS.2019.2956544","volume":"7","author":"Q Liu","year":"2019","unstructured":"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\u2013177503 (2019)","journal-title":"IEEE Access"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"L. Pellaco, N. Singh, J. Jald\u00e9n, Spectrum prediction and interference detection for satellite communications, in IET International Communications Satellite Systems Conference (2019), pp. 1\u20138","DOI":"10.1049\/cp.2019.1269"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"P. Henarejos, M.A. V\u00e1zquez, A.I. P\u00e9rez-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\u20135","DOI":"10.1109\/SPAWC.2019.8815532"},{"issue":"16","key":"7_CR14","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1049\/iet-com.2018.5774","volume":"13","author":"X Hu","year":"2019","unstructured":"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\u20132491 (2019)","journal-title":"Wiley IET Commun."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"136655","DOI":"10.1109\/ACCESS.2020.3011746","volume":"8","author":"L Lei","year":"2020","unstructured":"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\u2013136667 (2020)","journal-title":"IEEE Acccess"},{"issue":"4","key":"7_CR16","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1109\/LCOMM.2022.3141420","volume":"26","author":"X Hu","year":"2022","unstructured":"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\u2013812 (2022)","journal-title":"IEEE Commun. Lett."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"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\u201349","DOI":"10.1145\/3376897.3377864"},{"issue":"6","key":"7_CR18","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/LWC.2020.2970711","volume":"9","author":"H Tsuchida","year":"2020","unstructured":"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\u2013812 (2020)","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"7_CR19","first-page":"6219","volume":"8","author":"B Zhao","year":"2020","unstructured":"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\u201362206 (2020)","journal-title":"IEEE Access"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"34874","DOI":"10.1109\/ACCESS.2018.2847045","volume":"6","author":"C Han","year":"2018","unstructured":"C. Han, Y. Niu, Cross-layer anti-jamming scheme: a hierarchical learning approach. IEEE Access 6, 34874\u201334883 (2018)","journal-title":"IEEE Access"},{"issue":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s11276-017-1551-9","volume":"25","author":"F Yao","year":"2019","unstructured":"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\u2013213 (2019)","journal-title":"Springer Wirel. Netw."},{"issue":"10","key":"7_CR22","doi-asserted-by":"publisher","first-page":"9499","DOI":"10.1109\/TVT.2018.2856854","volume":"67","author":"L Xiao","year":"2018","unstructured":"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\u20139512 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"5","key":"7_CR23","doi-asserted-by":"publisher","first-page":"5331","DOI":"10.1109\/TVT.2020.2982672","volume":"69","author":"C Han","year":"2020","unstructured":"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\u20135342 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"3","key":"7_CR24","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1109\/TAES.2017.2671247","volume":"53","author":"T Yairi","year":"2017","unstructured":"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\u20131401 (2017)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"issue":"4","key":"7_CR25","doi-asserted-by":"publisher","first-page":"1816","DOI":"10.1109\/TAES.2018.2876586","volume":"55","author":"SK Ibrahim","year":"2018","unstructured":"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\u20131827 (2018)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"7_CR26","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1109\/ACCESS.2019.2962235","volume":"8","author":"P Wan","year":"2019","unstructured":"P. Wan, Y. Zhan, W. Jiang, Study on the satellite telemetry data classification based on self-learning. IEEE Access 8, 2656\u20132669 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"7_CR27","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MCOM.001.2100818","volume":"60","author":"B Zhao","year":"2022","unstructured":"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\u201336 (2022)","journal-title":"IEEE Commun. Mag."},{"issue":"5","key":"7_CR28","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/JSAC.2019.2906789","volume":"37","author":"N Cheng","year":"2019","unstructured":"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\u20131129 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"55915","DOI":"10.1109\/ACCESS.2020.2982356","volume":"8","author":"G Cui","year":"2020","unstructured":"G. Cui, X. Li, L. Xu, W. Wang, Latency and energy optimization for MEC enhanced SAT-IoT networks. IEEE Access 8, 55915\u201355926 (2020)","journal-title":"IEEE Access"},{"issue":"12","key":"7_CR30","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MAES.2020.3008468","volume":"35","author":"G Furano","year":"2020","unstructured":"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\u201356 (2020)","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"7_CR31","doi-asserted-by":"publisher","first-page":"5115","DOI":"10.1109\/JSTARS.2020.3018719","volume":"13","author":"AS Li","year":"2020","unstructured":"A.S. Li, V. Chirayath, M. Segal-Rozenhaimer, J.L. Torres-Perez, J. van den Bergh, NASA NeMO-net\u2019s convolutional neural network: mapping marine habitats with spectrally heterogeneous remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 5115\u20135133 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"7_CR32","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1109\/JSTARS.2020.3031741","volume":"14","author":"G Mateo-Garc\u00eda","year":"2020","unstructured":"G. Mateo-Garc\u00eda, V. Laparra, D. L\u00f3pez-Puigdollers, L. G\u00f3mez-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\u2013761 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"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\u20138","DOI":"10.1109\/IJCNN48605.2020.9207603"},{"issue":"4","key":"7_CR34","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/MWC.2018.1800365","volume":"26","author":"N Kato","year":"2019","unstructured":"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\u2013147 (2019)","journal-title":"IEEE Wirel. Commun."},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/GLOBECOM42002.2020.9348105"},{"issue":"7","key":"7_CR36","doi-asserted-by":"publisher","first-page":"4685","DOI":"10.1109\/TWC.2020.2986114","volume":"19","author":"C Jiang","year":"2020","unstructured":"C. Jiang, X. Zhu, Reinforcement learning based capacity management in multi-layer satellite networks. IEEE Trans. Wirel. Commun. 19(7), 4685\u20134699 (2020)","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"10","key":"7_CR37","doi-asserted-by":"publisher","first-page":"5106","DOI":"10.3390\/app12105106","volume":"12","author":"A Russo","year":"2022","unstructured":"A. Russo, G. Lax, Using artificial intelligence for space challenges: A survey. Appl. Sci. 12.10, 5106, (2022)","journal-title":"Appl. Sci."},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"G. Labr\u00e8che, 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\u201317","DOI":"10.1109\/AERO53065.2022.9843402"}],"container-title":["Signals and Communication Technology","A Roadmap to Future Space Connectivity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30762-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T16:03:47Z","timestamp":1689091427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30762-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031307614","9783031307621"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30762-1_7","relation":{},"ISSN":["1860-4862","1860-4870"],"issn-type":[{"type":"print","value":"1860-4862"},{"type":"electronic","value":"1860-4870"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"6 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}