doi:10.23919/ICN.2021.0015

2021, Vol. 2

Issue (3): 213-243 doi: 10.23919/ICN.2021.0015

Download:

PDF (1337 KB)

HTML
Export: BibTeX | EndNote (RIS)

Received: 25 January 2021 Online: 07 December 2021


	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

. . , 2021, 2: 213-243.

URL:

http://icn.tsinghuajournals.com/10.23919/ICN.2021.0015 OR http://icn.tsinghuajournals.com/Y2021/V2/I3/213

Fig.1

Tab.1

Tab.2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Tab.3


1	G. Maral, M. Bousquet, and Z. L. Sun, Satellite Communications Systems: Systems, Techniques and Technology. 6th ed. West Sussex, UK: John Wiley & Sons, 2020.
2	F. Rinaldi, H. L. Maattanen, J. Torsner, S. Pizzi, S. Andreev, A. Iera, Y. Koucheryavy, and G. Araniti Non-terrestrial networks in 5G & beyond: A survey[J]. IEEE Access, 2020, 8: 165178- 165200 doi: 10.1109/ACCESS.2020.3022981
3	P. K. Chowdhury, M. Atiquzzaman, and W. Ivancic Handover schemes in satellite networks: State-of-the-art and future research directions[J]. IEEE Commun. Surv. Tutorials, 2006, 8 (4): 2- 14 doi: 10.1109/COMST.2006.283818
4	P. Chini, G. Giambene, and S. Kota A survey on mobile satellite systems[J]. Int. J. Satell. Commun. Netw., 2010, 28 (1): 29- 57 doi: 10.1002/sat.941
5	P. D. Arapoglou, K. Liolis, M. Bertinelli, A. Panagopoulos, P. Cottis, and R. De Gaudenzi MIMO over satellite: A review[J]. IEEE Commun. Surv. Tutorials, 2011, 13 (1): 27- 51 doi: 10.1109/SURV.2011.033110.00072
6	M. De Sanctis, E. Cianca, G. Araniti, I. Bisio, and R. Prasad Satellite communications supporting internet of remote things[J]. IEEE Int. Things J., 2016, 3 (1): 113- 123 doi: 10.1109/JIOT.2015.2487046
7	R. Radhakrishnan, W. W. Edmonson, F. Afghah, R. M. Rodriguez-Osorio, F. Pinto, and S. C. Burleigh Survey of inter-satellite communication for small satellite systems: Physical layer to network layer view[J]. IEEE Commun. Surv. Tutorials, 2016, 18 (4): 2442- 2473 doi: 10.1109/COMST.2016.2564990
8	C. Niephaus, M. Kretschmer, and G. Ghinea QoS provisioning in converged satellite and terrestrial networks: A survey of the state-of-the-art[J]. IEEE Commun. Surv. Tutorials, 2016, 18 (4): 2415- 2441 doi: 10.1109/COMST.2016.2561078
9	H. Kaushal and G. Kaddoum Optical communication in space: Challenges and mitigation techniques[J]. IEEE Commun. Surv. Tutorials, 2017, 19 (1): 57- 96 doi: 10.1109/COMST.2016.2603518
10	J. J. Liu, Y. P. Shi, Z. M. Fadlullah, and N. Kato Space-air-ground integrated network: A survey[J]. IEEE Commun. Surv. Tutorials, 2018, 20 (4): 2714- 2741 doi: 10.1109/COMST.2018.2841996
11	S. C. Burleigh, T. De Cola, S. Morosi, S. Jayousi, E. Cianca, and C. Fuchs From connectivity to advanced internet services: A comprehensive review of small satellites communications and networks[J]. Wirel. Commun. Mob. Comput., 2019, 2019 (11): 6243505
12	B. Li, Z. S. Fei, C. Q. Zhou, and Y. Zhang Physical-layer security in space information networks: A survey[J]. IEEE Int. Things J., 2020, 7 (1): 33- 52 doi: 10.1109/JIOT.2019.2943900
13	N. Saeed, A. Elzanaty, H. Almorad, H. Dahrouj, T. Y. Al-Naffouri, and M. S. Alouini CubeSat communications: Recent advances and future challenges[J]. IEEE Commun. Surv. Tutorials, 2020, 22 (3): 1839- 1862 doi: 10.1109/COMST.2020.2990499
14	O. Simeone A very brief introduction to machine learning with applications to communication systems[J]. IEEE Trans. Cogn. Commun. Netw., 2018, 4 (4): 648- 664 doi: 10.1109/TCCN.2018.2881442
15	M. Z. Chen, U. Challita, W. Saad, C. C. Yin, and M. Debbah Artificial neural networks-based machine learning for wireless networks: A tutorial[J]. IEEE Commun. Surv. Tutorials, 2019, 21 (4): 3039- 3071 doi: 10.1109/COMST.2019.2926625
16	Y. C. Qian, J. Wu, R. Wang, F. S. Zhu, and W. Zhang Survey on reinforcement learning applications in communication networks[J]. J. Commun. Inform. Netw., 2019, 4 (2): 30- 39
17	E. C. Strinati, S. Barbarossa, J. L. Gonzalez-Jimenez, D. Ktenas, N. Cassiau, L. Maret, and C. Dehos 6G: The next frontier: From holographic messaging to artificial intelligence using subterahertz and visible light communication[J]. IEEE Veh. Technol. Mag., 2019, 14 (3): 42- 50 doi: 10.1109/MVT.2019.2921162
18	J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, and T. Melodia Machine learning for wireless communications in the Internet of Things: A comprehensive survey[J]. Ad Hoc Networks, 2019, 93: 101913 doi: 10.1016/j.adhoc.2019.101913
19	G. P. Kumar and P. Venkataram Artificial intelligence approaches to network management: recent advances and a survey[J]. Comput. Commun., 1997, 20 (15): 1313- 1322 doi: 10.1016/S0140-3664(97)00094-7
20	Y. L. Zou, J. Zhu, X. B. Wang, and L. Hanzo A survey on wireless security: Technical challenges, recent advances, and future trends[J]. Proc. IEEE, 2016, 104 (9): 1727- 1765
21	S. H. Alsamhi, O. Ma, and M. S. Ansari Survey on artificial intelligence based techniques for emerging robotic communication[J]. Telecommun. Syst., 2019, 72 (3): 483- 503 doi: 10.1007/s11235-019-00561-z
22	H. M. El Misilmani and T. Naous, Machine learning in antenna design: An overview on machine learning concept and algorithms, presented at 2019 Int. Conf. High Performance Computing & Simulation (HPCS), Dublin, Ireland, 2019, pp. 600–607.
23	P. S. Bithas, E. T. Michailidis, N. Nomikos, D. Vouyioukas, and A. G. Kanatas A survey on machinelearning techniques for UAV-based communications[J]. Sensors, 2019, 19 (23): 5170 doi: 10.3390/s19235170
24	M. A. Lahmeri, M. A. Kishk, and M. S. Alouini Artificial intelligence for UAV-enabled wireless networks: A survey[J]. IEEE Open J. Commun. Soc., 2021, 2: 1015- 1040 doi: 10.1109/OJCOMS.2021.3075201
25	M. á. Vázquez, P. Henarejos, A. I. Pérez-Neira, E. Grechi, A. Voight, J. C. Gil, I. Pappalardo, F. Di Credico, and R. M. Lancellotti, On the use of AI for satellite communications, arXiv preprint arXiv: 2007.10110, 2020.
26	N. Kato, Z. M. Fadlullah, F. X. Tang, B. M. Mao, S. Tani, A. Okamura, and J. J. Liu Optimizing space-air-ground integrated networks by artificial intelligence[J]. IEEE Wirel. Commun., 2019, 26 (4): 140- 147 doi: 10.1109/MWC.2018.1800365
27	V. Kothari, E. Liberis, and N. D. Lane, The final frontier: Deep learning in space, in Proc. 21^st Int. Workshop on Mobile Computing Systems and Applications, Austin, TX, USA, 2020, pp. 45–49.
28	F. Chollet, Deep learning with Python. New York, NY, USA: Simon and Schuster, 2017.
29	B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, in Proc. 5^th Annu. Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 1992, pp. 144–152.
30	C. M. Bishop, Pattern recognition and Machine Learning. New York, NY, USA: Springer, 2006.
31	J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez A comprehensive survey on support vector machine classification: Applications, challenges and trends[J]. Neurocomputing, 2020, 408: 189- 215
32	J. R. Quinlan Induction of decision trees[J]. Mach. Learn., 1986, 1 (1): 81- 106
33	L. Breiman Random forests[J]. Mach. Learn., 2001, 45 (1): 5- 32 doi: 10.1023/A:1010933404324
34	L. Breiman Bagging predictors[J]. Mach. Learn., 1996, 24 (2): 123- 140
35	J. H. Friedman Greedy function approximation: a gradient boosting machine[J]. Ann. Statist., 2001, 29: 1189- 1232 doi: 10.1214/aos/1013203450
36	XGBoost documentation, https://xgboost.readthedocs.io/en/latest/, 2021.
37	T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, K. Chen, R. Mitchell, I. Cano, T. Zhou, et al XGBoost: Extreme gradient boosting[J]. R package version 0.4–2, 2015, 1 (4): 1- 4
38	Kaggle, https://www.kaggle.com/, 2021.
39	P. Baldi and K. Hornik Neural networks and principal component analysis: Learning from examples without local minima[J]. Neural Networks, 1989, 2 (1): 53- 58 doi: 10.1016/0893-6080(89)90014-2
40	R. Hecht-Nielsen, Theory of the backpropagation neural network, in Neural Networks for Perception (Vol. 2): Computation, Learning, Architectures. San Diego, CA, USA: Harcourt Brace & Co., 1992, pp. 65–93.
41	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
42	S. Albawi, T. A. Mohammed, and S. Al-Zawi, Understanding of a convolutional neural network, presented at 2017 Int. Conf. Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1–6.
43	T. He, Z. Zhang, H. Zhang, Z. Y. Zhang, J. Y. Xie, and M. Li, Bag of tricks for image classification with convolutional neural networks, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 558–567.
44	Z. X. Zou, Z. W. Shi, Y. H. Guo, and J. P. Ye, Object detection in 20 years: A survey, arXiv preprint arXiv: 1905.05055, 2019.
45	Q. Chu, W. L. Ouyang, H. S. Li, X. G. Wang, B. Liu, and N. H. Yu, Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism, in Proc. 2017 IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 4846–4855.
46	K. R. Chowdhary, Natural language processing, in Fundamentals of Artificial Intelligence, K. R. Chowdhary, ed. Springer, 2020, pp. 603–649.
47	Y. S. Wang, H. X. Yao, and S. C. Zhao Auto-encoder based dimensionality reduction[J]. Neurocomputing, 2016, 184: 232- 242 doi: 10.1016/j.neucom.2015.08.104
48	C. Zhou and R. C. Paffenroth, Anomaly detection with robust deep autoencoders, in Proc. 23^rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 665–674.
49	C. Doersch, Tutorial on variational autoencoders, arXiv preprint arXiv: 1606.05908, 2021.
50	I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27^th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.
51	A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath Generative adversarial networks: An overview[J]. IEEE Signal Process. Mag., 2018, 35 (1): 53- 65 doi: 10.1109/MSP.2017.2765202
52	R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
53	D. D. Margineantu and T. G. Dietterich, Pruning adaptive boosting, in Proc. 14^th Int. Conf. Machine Learning, Nashville, TN, USA, 1997, pp. 211–218.
54	J. Snoek, H. Larochelle, and R. P. Adams, Practical Bayesian optimization of machine learning algorithms, in Proc. 25^th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2012, pp. 2951–2959.
55	M. á. Vázquez, A. Pérez-Neira, D. Christopoulos, S. Chatzinotas, B. Ottersten, P.-D. Arapoglou, A. Ginesi, and G. Taricco, Precoding in multibeam satellite communications: Present and future challenges, IEEE Wireless Communications, vol. 23, no. 6, pp. 88–95, 2016.
56	A. Freedman, D. Rainish, and Y. Gat, Beam hopping how to make it possible, in Proc. Ka and Broadband Communication Conf., Bologna, Italy, 2015, p. 6.
57	L. Lei, E. Lagunas, Y. X. Yuan, M. G. Kibria, S. Chatzinotas, and B. Ottersten Beam illumination pattern design in satellite networks: Learning and optimization for efficient beam hopping[J]. IEEE Access, 2020, 8: 136655- 136667 doi: 10.1109/ACCESS.2020.3011746
58	P. Angeletti, D. F. Prim, and R. Rinaldo, Beam hopping in multi-beam broadband satellite systems: System performance and payload architecture analysis, presented at 24^th AIAA Int. Communications Satellite Systems Conf., San Diego, CA, USA, 2006, p. 5376.
59	J. Anzalchi, A. Couchman, P. Gabellini, G. Gallinaro, L. D’Agristina, N. Alagha, and P. Angeletti, Beam hopping in multi-beam broadband satellite systems: System simulation and performance comparison with non-hopped systems, presented at 2010 5^th Advanced Satellite Multimedia Systems Conf. and the 11th Signal Processing for Space Communications Workshop, Cagliari, Italy, 2010, pp. 248–255.
60	X. Alberti, J. M. Cebrian, A. Del Bianco, Z. Katona, J. Lei, M. A. Vazquez-Castro, A. Zanus, L. Gilbert, and N. Alagha, System capacity optimization in time and frequency for multibeam multi-media satellite systems, presented at 2010 5^th Advanced Satellite Multimedia Systems Conf. and the 11^th Signal Processing for Space Communications Workshop, Cagliari, Italy, 2010, pp. 226–233.
61	B. Evans and P. Thompson, Key issues and technologies for a terabit/s satellite, presented at 28^th AIAA Int. Communications Satellite Systems Conf. (ICSSC-2010), Anaheim, CA, USA, 2010, pp. 92–102.
62	J. Lei and M. á. Vázquez-Castro Multibeam satellite frequency/time duality study and capacity optimization[J]. J. Commun. Netw., 2011, 13 (5): 472- 480 doi: 10.1109/JCN.2011.6112304
63	R. Alegre, N. Alagha, and M. á. Vázquez-Castro, Heuristic algorithms for flexible resource allocation in beam hopping multi-beam satellite systems, presented at 29^th AIAA Int. Communications Satellite Systems Conf. (ICSSC-2011), Nara, Japan, 2011, p. 8001.
64	R. Alegre-Godoy, N. Alagha, and M. A. Vázquez-Castro, Offered capacity optimization mechanisms for multi-beam satellite systems, presented at 2012 IEEE Int. Conf. Communications (ICC), Ottawa, Canada, 2012, pp. 3180–3184.
65	H. Y. Liu, Z. M. Yang, and Z. C. Cao Max-min rate control on traffic in broadband multibeam satellite communications systems[J]. IEEE Commun. Lett., 2013, 17 (7): 1396- 1399 doi: 10.1109/LCOMM.2013.052013.130700
66	H. Han, X. Q. Zheng, Q. F. Huang, and Y. Lin QoS-equilibrium slot allocation for beam hopping in broadband satellite communication systems[J]. Wirel. Netw., 2015, 21 (8): 2617- 2630 doi: 10.1007/s11276-015-0934-z
67	S. C. Shi, G. X. Li, Z. Q. Li, H. P. Zhu, and B. Gao Joint power and bandwidth allocation for beam-hopping user downlinks in smart gateway multibeam satellite systems[J]. Int. J. Distrib. Sens. Netw., 2017, 13 (5): 1- 11
68	A. Ginesi, E. Re, and P. D. Arapoglou, Joint beam hopping and precoding in HTS systems, presented at 9^th Int. Conf. Wireless and Satellite Systems, Oxford, UK, 2017, pp. 43–51.
69	G. Cocco, T. De Cola, M. Angelone, Z. Katona, and S. Erl Radio resource management optimization of flexible satellite payloads for DVB-S2 systems[J]. IEEE Trans. Broadcast., 2018, 64 (2): 266- 280 doi: 10.1109/TBC.2017.2755263
70	X. Hu, S. J. Liu, Y. P. Wang, L. X. Xu, Y. C. Zhang, C. Wang, and W. D. Wang Deep reinforcement learning-based beam hopping algorithm in multibeam satellite systems[J]. IET Commun., 2019, 13 (16): 2485- 2491 doi: 10.1049/iet-com.2018.5774
71	Y. C. Zhang, X. Hu, R. Chen, Z. L. Zhang, L. Q. Wang, and W. D. Wang, Dynamic beam hopping for DVB-S2X satellite: A multi-objective deep reinforcement learning approach, presented at 2019 IEEE Int. Conf. Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China, 2019, pp. 164–169.
72	X. Hu, Y. C. Zhang, X. L. Liao, Z. J. Liu, W. D. Wang, and F. M. Ghannouchi Dynamic beam hopping method based on multi-objective deep reinforcement learning for next generation satellite broadband systems[J]. IEEE Trans. Broadcast., 2020, 66 (3): 630- 646 doi: 10.1109/TBC.2019.2960940
73	M. K. Simon, J. K. Omura, R. A. Scholtz, and B. K. Levitt, Spread Spectrum Communications Handbook, Electronic Edition. New York, NY, USA: McGraw-Hill, 2002.
74	D. Torrieri, Principles of Spread-Spectrum Communication Systems. Berlin, Germany: Springer, 2015.
75	S. Bae, S. Kim, and J. Kim Efficient frequency-hopping synchronization for satellite communications using dehop-rehop transponders[J]. IEEE Trans. Aerosp. Electron. Syst., 2016, 52 (1): 261- 274 doi: 10.1109/TAES.2015.150062
76	F. Q. Yao, L. L. Jia, Y. M. Sun, Y. H. Xu, S. Feng, and Y. G. Zhu A hierarchical learning approach to anti-jamming channel selection strategies[J]. Wirel. Netw., 2019, 25 (1): 201- 213 doi: 10.1007/s11276-017-1551-9
77	L. Xiao, D. H. Jiang, D. J. Xu, H. Z. Zhu, Y. Y. Zhang, and H. V. Poor Two-dimensional antijamming mobile communication based on reinforcement learning[J]. IEEE Trans. Veh. Technol., 2018, 67 (10): 9499- 9512 doi: 10.1109/TVT.2018.2856854
78	C. Han and Y. T. Niu Cross-layer anti-jamming scheme: A hierarchical learning approach[J]. IEEE Access, 2018, 6: 34874- 34883 doi: 10.1109/ACCESS.2018.2847045
79	S. Lee, S. Kim, M. Seo, and D. Har Synchronization of frequency hopping by LSTM network for satellite communication system[J]. IEEE Commun. Lett., 2019, 23 (11): 2054- 2058 doi: 10.1109/LCOMM.2019.2936019
80	C. Han, L. Y. Huo, X. H. Tong, H. C. Wang, and X. Liu Spatial anti-jamming scheme for internet of satellites based on the deep reinforcement learning and stackelberg game[J]. IEEE Trans. Veh. Technol., 2020, 69 (5): 5331- 5342 doi: 10.1109/TVT.2020.2982672
81	C. Han, A. J. Liu, H. C. Wang, L. Y. Huo, and X. H. Liang Dynamic anti-jamming coalition for satellite-enabled army IoT: A distributed game approach[J]. IEEE Int. Things J., 2020, 7 (11): 10932- 10944 doi: 10.1109/JIOT.2020.2991585
82	Y. X. Bie, L. Z. Wang, Y. Tian, and Z. Hu A combined forecasting model for satellite network self-similar traffic[J]. IEEE Access, 2019, 7: 152004- 152013 doi: 10.1109/ACCESS.2019.2944895
83	L. Rossi, J. Chakareski, P. Frossard, and S. Colonnese A Poisson hidden Markov model for multiview video traffic[J]. IEEE/ACM Trans. Netw., 2015, 23 (2): 547- 558 doi: 10.1109/TNET.2014.2303162
84	D. Yan and L. Y. Wang, TPDR: Traffic prediction based dynamic routing for LEO&GEO satellite networks, presented at 2015 IEEE 5^th Int. Conf. Electronics Information and Emergency Communication, Beijing, China, 2015, pp. 104–107.
85	F. L. Xu, Y. Y. Lin, J. X. Huang, D. Wu, H. Z. Shi, J. Song, and Y. Li Big data driven mobile traffic understanding and forecasting: A time series approach[J]. IEEE Trans. Serv. Comput., 2016, 9 (5): 796- 805 doi: 10.1109/TSC.2016.2599878
86	C. Katris and S. Daskalaki Comparing forecasting approaches for internet traffic[J]. Expert Syst. Appl., 2015, 42 (21): 8172- 8183 doi: 10.1016/j.eswa.2015.06.029
87	B. Gao, Q. Y. Zhang, Y. S. Liang, N. N. Liu, C. B. Huang, and N. T. Zhang Predicting self-similar networking traffic based on EMD and ARMA, (in Chinese)[J]. J. Commun., 2011, 32 (4): 47- 56
88	X. Q. Pan, W. S. Zhou, Y. Lu, and N. Sun Prediction of network traffic of smart cities based on DE-BP neural network[J]. IEEE Access, 2019, 7: 55807- 55816 doi: 10.1109/ACCESS.2019.2913017
89	J. X. Liu and Z. H. Jia Telecommunication traffic prediction based on improved LSSVM[J]. Int. J. Patt. Recogn. Artif. Intell., 2018, 32 (3): 1850007 doi: 10.1142/S0218001418500076
90	Z. L. Liu and X. Li Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks[J]. J. China Univ. Posts Telecommun., 2018, 25 (1): 15- 28, 36
91	Z. Na, Z. Pan, X. Liu, Z. A. Deng, Z. H. Gao, and Q. Guo Distributed routing strategy based on machine learning for LEO satellite network[J]. Wirel. Commun. Mob. Comput., 2018, 2018: 3026405
92	G. B. Huang, Q. Y. Zhu, and C. K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, presented at 2004 IEEE Int. Joint Conf. Neural Networks (IEEE Cat. No.04CH37541), Budapest, Hungary, 2004, pp. 985–990.
93	A. Goldsmith, Wireless Communications. Cambridge, MA, USA: Cambridge University Press, 2005.
94	T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design[J]. IEEE Trans. Commun., 2015, 63 (9): 3029- 3056 doi: 10.1109/TCOMM.2015.2434384
95	S. Sangodoyin, S. Niranjayan, and A. F. Molisch A measurement-based model for outdoor near-ground ultrawideband channels[J]. IEEE Trans. Antennas Propag., 2016, 64 (2): 740- 751 doi: 10.1109/TAP.2015.2505004
96	C. X. Wang, J. Bian, J. Sun, W. S. Zhang, and M. G. Zhang A survey of 5G channel measurements and models[J]. IEEE Commun. Surv. Tutorials, 2018, 20 (4): 3142- 3168 doi: 10.1109/COMST.2018.2862141
97	B. Ai, K. Guan, R. S. He, J. Z. Li, G. K. Li, D. P. He, Z. D. Zhong, and K. M. S. Huq On indoor millimeter wave massive MIMO channels: Measurement and simulation[J]. IEEE J. Select. Areas Commun., 2017, 35 (7): 1678- 1690 doi: 10.1109/JSAC.2017.2698780
98	G. Liang and H. L. Bertoni A new approach to 3-D ray tracing for propagation prediction in cities[J]. IEEE Trans. Antennas Propag., 1998, 46 (6): 853- 863 doi: 10.1109/8.686774
99	M. F. Zhu, A. Singh, and F. Tufvesson, Measurement based ray launching for analysis of outdoor propagation, presented at 2012 6^th European Conf. Antennas and Propagation (EUCAP), Prague, Czech Republic, 2012, pp. 3332–3336.
100	Z. Q. Yun and M. F. Iskander Ray tracing for radio propagation modeling: Principles and applications[J]. IEEE Access, 2015, 3: 1089- 1100 doi: 10.1109/ACCESS.2015.2453991
101	D. Cichon and T. Kümer, Propagation prediction models, in Digital Mobile Radio towards Future Generation Systems. E Damosso and L M Correia, eds. Belgium, Brussels: European Commisson, 1999, pp. 115–208.
102	L. C. Fernandes and A. J. M. Soares Simplified characterization of the urban propagation environment for path loss calculation[J]. IEEE Antennas Wirel. Propag. Lett., 2010, 9: 24- 27 doi: 10.1109/LAWP.2010.2041523
103	L. C. Fernandes and A. J. M. Soares, On the use of image segmentation for propagation path loss prediction, presented at 2011 SBMO/IEEE MTT-S Int. Microwave and Optoelectronics Conf. (IMOC 2011), Natal, Brazil, 2011, pp. 129–133.
104	M. Piacentini and F. Rinaldi Path loss prediction in urban environment using learning machines and dimensionality reduction techniques[J]. Comput. Manag. Sci., 2011, 8 (4): 371- 385 doi: 10.1007/s10287-010-0121-8
105	M. Uccellari, F. Facchini, M. Sola, E. Sirignano, G. M. Vitetta, A. Barbieri, and S. Tondelli, On the use of support vector machines for the prediction of propagation losses in smart metering systems, presented at 2016 IEEE 26^th Int. Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Italy, 2016, pp. 1–6.
106	S. P. Sotiroudis, S. K. Goudos, K. A. Gotsis, K. Siakavara, and J. N. Sahalos Application of a composite differential evolution algorithm in optimal neural network design for propagation path-loss prediction in mobile communication systems[J]. IEEE Antennas Wirel. Propag. Lett., 2013, 12: 364- 367 doi: 10.1109/LAWP.2013.2251994
107	S. P. Sotiroudis and K. Siakavara Mobile radio propagation path loss prediction using artificial neural networks with optimal input information for urban environments[J]. AEU-Int. J. Electron. Commun., 2015, 69 (10): 1453- 1463 doi: 10.1016/j.aeue.2015.06.014
108	I. Popescu, I. Nafornita, and P. Constantinou, Comparison of neural network models for path loss prediction, presented at WiMob’2005, IEEE Int. Conf. Wireless and Mobile Computing, Networking and Communications, Montreal, Canada, 2005, pp. 44–49.
109	E. Ostlin, H. J. Zepernick, and H. Suzuki Macrocell path-loss prediction using artificial neural networks[J]. IEEE Trans. Veh. Technol., 2010, 59 (6): 2735- 2747 doi: 10.1109/TVT.2010.2050502
110	B. J. Cavalcanti, G. A. Cavalcante, L. M. de Mendon?a, G. M. Cantanhede, M. M. M. de Oliveira, and A. G. D’Assun??o A hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz[J]. J. Microw. Optoelectron. Electromagn. Appl., 2017, 16 (3): 708- 722 doi: 10.1590/2179-10742017v16i3925
111	Y. Zhang, J. X. Wen, G. S. Yang, Z. W. He, and X. R. Luo Air-to-air path loss prediction based on machine learning methods in urban environments[J]. Wirel. Commun. Mob. Comput., 2018, 2018: 8489326
112	C. A. Oroza, Z. R. Zhang, T. Watteyne, and S. D. Glaser A machine-learning-based connectivity model for complex terrain large-scale low-power wireless deployments[J]. IEEE Trans. Cogn. Commun. Netw., 2017, 3 (4): 576- 584 doi: 10.1109/TCCN.2017.2741468
113	Y. Zhang, J. X. Wen, G. S. Yang, Z. W. He, and J. Wang Path loss prediction based on machine learning: Principle, method, and data expansion[J]. Appl. Sci., 2019, 9 (9): 1908
114	H. F. Ates, S. M. Hashir, T. Baykas, and B. K. Gunturk Path loss exponent and shadowing factor prediction from satellite images using deep learning[J]. IEEE Access, 2019, 7: 101366- 101375 doi: 10.1109/ACCESS.2019.2931072
115	J. Thrane, D. Zibar, and H. L. Christiansen Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz[J]. IEEE Access, 2020, 8: 7925- 7936 doi: 10.1109/ACCESS.2020.2964103
116	O. Ahmadien, H. F. Ates, T. Baykas, and B. K. Gunturk Predicting path loss distribution of an area from satellite images using deep learning[J]. IEEE Access, 2020, 8: 64982- 64991 doi: 10.1109/ACCESS.2020.2985929
117	T. Yairi, N. Takeishi, T. Oda, Y. Nakajima, N. Nishimura, and N. Takata A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction[J]. IEEE Trans. Aerosp. Electron. Syst., 2017, 53 (3): 1384- 1401 doi: 10.1109/TAES.2017.2671247
118	T. Yairi, T. Tagawa, and N. Takata, Telemetry monitoring by dimensionality reduction and learning hidden Markov model, presented at Int. Symp. Artificial Intelligence, Robotics and Automation in Space, Turin, Italy, 2012.
119	T. Yairi, M. Nakatsugawa, K. Hori, S. Nakasuka, K. Machida, and N. Ishihama, Adaptive limit checking for spacecraft telemetry data using regression tree learning, presented at 2004 IEEE Int. Conf. Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), the Hague, the Netherlands, 2004, pp. 5130–5135.
120	S. Tariq, S. Lee, Y. Shin, M. S. Lee, O. Jung, D. Chung, and S. S. Woo, Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA, in Proc. 25^th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 2123–2133.
121	K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding, in Proc. 24^th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, London, UK, 2018, pp. 387–395.
122	S. Fuertes, G. Picart, J. Y. Tourneret, L. Chaari, A. Ferrari, and C. Richard, Improving spacecraft health monitoring with automatic anomaly detection techniques, presented at SpaceOps 2016 Conf., Daejeon, Republic of Korea, 2016, p. 1.
123	D. L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor, R. Mackey, J. P. Castle, and V. Baskaran General purpose data-driven monitoring for space operations[J]. J. Aerosp. Comput. Inform. Commun., 2012, 9 (2): 26- 44 doi: 10.2514/1.54964
124	P. Robinson, M. Shirley, D. Fletcher, R. Alena, D. Duncavage, and C. Lee, Applying model-based reasoning to the FDIR of the command & data handling subsystem of the international space station, presented at 7^th Int. Symp. Artificial Intelligence, Robotics and Automation in Space: i-SAIRAS 2003, Nara, Japan, 2003.
125	Y. Y. Sun, L. L. Guo, Y. M. Wang, Z. S. Ma, and Y. Niu Fault diagnosis for space utilisation[J]. J. Eng., 2019, 2019 (23): 8770- 8775 doi: 10.1049/joe.2018.9102
126	S. K. Ibrahim, A. Ahmed, M. A. E. Zeidan, and I. E. Ziedan Machine learning methods for spacecraft telemetry mining[J]. IEEE Trans. Aerosp. Electron. Syst., 2019, 55 (4): 1816- 1827 doi: 10.1109/TAES.2018.2876586
127	P. Wan, Y. F. Zhan, and W. W. Jiang Study on the satellite telemetry data classification based on self-learning[J]. IEEE Access, 2020, 8: 2656- 2669 doi: 10.1109/ACCESS.2019.2962235
128	P. W. Ward, J. W. Betz, and C. J. Hegarty, Satellite signal acquisition, tracking, and data demodulation, in Understanding GPS: Principles and Applications, 2nd ed., C. J. Hegarty, ed. London, UK: Artech House, 2006, pp. 153–241.
129	A. J. Van Dierendonck, J. Klobuchar, and Q. Hua, Ionospheric scintillation monitoring using commercial single frequency C/A code receivers, in Proc. 6^th Int. Technical Meeting of Satellite Division of the Institute of Navigation (ION GPS 1993), Salt Lake City, UT, USA, 1993, pp. 1333–1342.
130	J. Lee, Y. T. J. Morton, J. Lee, H. S. Moon, and J. Seo Monitoring and mitigation of ionospheric anomalies for GNSS-based safety critical systems: A review of up-to-date signal processing techniques[J]. IEEE Signal Process. Mag., 2017, 34 (5): 96- 110 doi: 10.1109/MSP.2017.2716406
131	C. Cesaroni, L. Alfonsi, R. Romero, N. Linty, F. Dovis, S. V. Veettil, J. Park, D. Barroca, M. C. Ortega, and R. O. Perez, Monitoring ionosphere over South America: The MImOSA and MImOSA2 projects, presented at 2015 Int. Association of Institutes of Navigation World Congress (IAIN), Prague, Czech Republic, 2015, pp. 1–7.
132	N. Linty, R. Romero, C. Cristodaro, F. Dovis, M. Bavaro, J. T. Curran, J. Fortuny-Guasch, J. Ward, G. Lamprecht, P. Riley, et al., Ionospheric scintillation threats to GNSS in polar regions: The DemoGRAPE case study in Antarctica, presented at 2016 European Navigation Conf. (ENC), Helsinki, Finland, 2016, pp. 1–7.
133	J. Vilà-Valls, P. Closas, C. Fernández-Prades, and J. T. Curran On the mitigation of ionospheric scintillation in advanced GNSS receivers[J]. IEEE Trans. Aerosp. Electron. Syst., 2018, 54 (4): 1692- 1708 doi: 10.1109/TAES.2018.2798480
134	S. Taylor, Y. Morton, Y. Jiao, J. Triplett, and W. Pelgrum, An improved ionosphere scintillation event detection and automatic trigger for a GNSS data collection system, in Proc. 2012 Int. Technical Meeting of the Institute of Navigation, Newport Beach, CA, USA, 2012, pp. 1563–1569.
135	W. X. Fu, S. W. Han, C. Rizos, M. Knight, and A. Finn, Real-time ionospheric scintillation monitoring, in Proc. 12^th Int. Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1999), Nashville, TN, USA, 1999, pp. 1461–1472.
136	S. Miriyala, P. R. Koppireddi, and S. R. Chanamallu Robust detection of ionospheric scintillations using MF-DFA technique[J]. Earth, Planets and Space, 2015, 67 (1): 98 doi: 10.1186/s40623-015-0268-1
137	R. Romero, N. Linty, F. Dovis, and R. V. Field, A novel approach to ionospheric scintillation detection based on an open loop architecture, presented at 2016 8^th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC), Noordwijk, the Netherlands, 2016, pp. 1–9.
138	L. F. C. Rezende, E. R. De Paula, S. Stephany, I. J. Kantor, M. T. A. H. Muella, P. M. de Siqueira, and K. S. Correa Survey and prediction of the ionospheric scintillation using data mining techniques[J]. Space Weather, 2010, 8 (6): S06D09
139	Y. Jiao, J. Hall, and Y. J. Morton, Performance evaluations of an equatorial GPS amplitude scintillation detector using a machine learning algorithm, in Proc. 29^th Int. Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, OR, USA, 2016, pp. 195–199.
140	Y. Jiao, J. J. Hall, and Y. T. Morton Automatic equatorial GPS amplitude scintillation detection using a machine learning algorithm[J]. IEEE Trans. Aerosp. Electron. Syst., 2017, 53 (1): 405- 418 doi: 10.1109/TAES.2017.2650758
141	Y. Jiao, J. Hall, and Y. J. Morton, Automatic GPS phase scintillation detector using a machine learning algorithm, in Proc. 2017 Int. Technical Meeting of the Institute of Navigation, Monterey, CA, USA, 2017, pp. 1160–1172.
142	Y. Jiao, J. J. Hall, and Y. T. Morton Performance evaluation of an automatic GPS ionospheric phase scintillation detector using a machine-learning algorithm[J]. J. Inst. Navig., 2017, 64 (3): 391- 402 doi: 10.1002/navi.188
143	N. Linty, A. Farasin, A. Favenza, and F. Dovis Detection of GNSS ionospheric scintillations based on machine learning decision tree[J]. IEEE Trans. Aerosp. Electron. Syst., 2019, 55 (1): 303- 317 doi: 10.1109/TAES.2018.2850385
144	R. Imam and F. Dovis, Distinguishing ionospheric scintillation from multipath in GNSS signals using bagged decision trees algorithm, presented at 2020 IEEE Int. Conf. Wireless for Space and Extreme Environments (WiSEE), Vicenza, Italy, 2020, pp. 83–88.
145	C. Politis, S. Maleki, C. Tsinos, S. Chatzinotas, and B. Ottersten, On-board the satellite interference detection with imperfect signal cancellation, presented at 2016 IEEE 17^th Int. Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, UK, 2016, pp. 1–5.
146	A. V. Dandawate and G. B. Giannakis Statistical tests for presence of cyclostationarity[J]. IEEE Trans. Signal Process., 1994, 42 (9): 2355- 2369 doi: 10.1109/78.317857
147	O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su Survey of automatic modulation classification techniques: Classical approaches and new trends[J]. IET Commun., 2007, 1 (2): 137- 156 doi: 10.1049/iet-com:20050176
148	J. Hu, D. M. Bian, Z. D. Xie, Y. Q. Li, and L. H. Fan, An approach for narrow band interference detection in satellite communication using morphological filter, presented at Int. Conf. Information Technology and Management Innovation, Shenzhen, China, 2015, pp. 1015–1020.
149	Q. Liu, J. Yang, C. J. Zhuang, A. Barnawi, and B. A. Alzahrani Artificial intelligence based mobile tracking and antenna pointing in satellite-terrestrial network[J]. IEEE Access, 2019, 7: 177497- 177503 doi: 10.1109/ACCESS.2019.2956544
150	L. Pellaco, N. Singh, and J. Jaldén, Spectrum prediction and interference detection for satellite communications, arXiv preprint arXiv: 1912.04716, 2019.
151	P. Henarejos, M. á. Vázquez, and A. I. Pérez-Neira, Deep learning for experimental hybrid terrestrial and satellite interference management, presented at 2019 IEEE 20^th Int. Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. 1–5.
152	N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov Deep learning classification of land cover and crop types using remote sensing data[J]. IEEE Geosci. Remote Sens. Lett., 2017, 14 (5): 778- 782 doi: 10.1109/LGRS.2017.2681128
153	F. Zhang, B. Du, and L. P. Zhang Scene classification via a gradient boosting random convolutional network framework[J]. IEEE Trans. Geosci. Remote Sens., 2016, 54 (3): 1793- 1802 doi: 10.1109/TGRS.2015.2488681
154	A. S. Li, V. Chirayath, M. Segal-Rozenhaimer, J. L. Torres-Pérez, and J. van den Bergh NASA NeMO-Net's convolutional neural network: Mapping marine habitats with spectrally heterogeneous remote sensing imagery[J]. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2020, 13: 5115- 5133 doi: 10.1109/JSTARS.2020.3018719
155	S. A. Fatima, A. Kumar, A. Pratap, and S. S. Raoof, Object recognition and detection in remote sensing images: a comparative study, presented at 2020 Int. Conf. Artificial Intelligence and Signal Processing (AISP), Amaravati, India, 2020, pp. 1–5.
156	L. M. Zhou, J. M. Liu, and L. Chen, Vehicle detection based on remote sensing image of YOLOv3, presented at 2020 IEEE 4^th Information Technology, Networking, Electronic and Automation Control Conf. (ITNEC), Chongqing, China, 2020, pp. 468–472.
157	J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788.
158	J. Redmon and A. Farhadi, YOLOv3: An incremental improvement, arXiv preprint arXiv: 1804.02767, 2018.
159	A. Femin and K. Biju, Accurate detection of buildings from satellite images using CNN, presented at 2020 Int. Conf. Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, 2020, pp. 1–5.
160	A. Hassan, W. M. Hussein, E. Said, and M. E. Hanafy, A deep learning framework for automatic airplane detection in remote sensing satellite images, presented at 2019 IEEE Aerospace Conf., Big Sky, MT, USA, 2019, pp. 1–10.
161	G. Mateo-García, V. Laparra, D. López-Puigdollers, and L. Gómez-Chova Cross-sensor adversarial domain adaptation of landsat-8 and Proba-V images for cloud detection[J]. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2021, 14: 747- 761 doi: 10.1109/JSTARS.2020.3031741
162	Z. F. Shao, Y. Pan, C. Y. Diao, and J. J. Cai Cloud detection in remote sensing images based on multiscale features-convolutional neural network[J]. IEEE Trans. Geosci. Remote Sens., 2019, 57 (6): 4062- 4076 doi: 10.1109/TGRS.2018.2889677
163	M. Tian, H. Chen, and G. H. Liu, Cloud detection and classification for S-NPP FSR CRIS data using supervised machine learning, presented at IGARSS 2019-2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, 2019, pp. 9827–9830.
164	F. Wang, F. S. Liao, and H. Q. Zhu, FPA-DNN: A forward propagation acceleration based deep neural network for ship detection, presented at 2020 Int. Joint Conf. Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.
165	Z. L. Li, L. Y. Wang, J. Y. Yu, B. W. Cheng, L. Hao, S. Jiang, Z. Li, and J. F. Yin, Remote sensing ship target detection and recognition system based on machine learning, presented at IGARSS 2019-2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, 2019, pp. 1272–1275.
166	H. Bandarupally, H. R. Talusani, and T. Sridevi, Detection of military targets from satellite images using deep convolutional neural networks, presented at 2020 IEEE 5^th Int. Conf. Computing Communication and Automation (ICCCA), Greater Noida, India, 2020, pp. 531–535.
167	J. H. Zheng, X. Y. Liu, and X. D. Wang Single image cloud removal using U-Net and generative adversarial networks[J]. IEEE Trans. Geosci. Remote Sens., 2021, 59 (8): 6371- 6385 doi: 10.1109/TGRS.2020.3027819
168	O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional networks for biomedical image segmentation, presented at 18^th Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, pp. 234–241.
169	J. Lu, Y. N. Chen, and R. J. He A learning-based approach for agile satellite onboard scheduling[J]. IEEE Access, 2020, 8: 16941- 16952 doi: 10.1109/ACCESS.2020.2968051
170	R. Mital, K. Cates, J. Coughlin, and G. Ganji, A machine learning approach to modeling satellite behavior, presented at 2019 IEEE Int. Conf. Space Mission Challenges for Information Technology (SMC-IT), Pasadena, CA, USA, 2019, pp. 62–69.
171	P. Dao, K. Weasenforth, J. Hollon, T. Payne, K. Kinateder, and A. Kruchten, Machine learning-based stability assessment and change detection for geosynchronous satellites, presented at the Advanced Maui Optical and Space Surveillance Technologies Conf., Maui, HI, USA, 2018.
172	B. Jia, K. D. Pham, E. Blasch, Z. H. Wang, D. Shen, and G. S. Chen, Space object classification using deep neural networks, presented at 2018 IEEE Aerospace Conf., Big Sky, MT, USA, 2018, pp. 1–8.
173	R. Chalapathy and C. Sanjay, Deep learning for anomaly detection: A survey,?arXiv preprint arXiv: 1901.03407, 2019.
174	B. Chen, J. W. Cao, A. Parra, and T. J. Chin, Satellite pose estimation with deep landmark regression and nonlinear pose refinement, in Proc. 2019 IEEE/CVF Int. Conf. Computer Vision Workshops (ICCVW), Seoul, Republic of Korea, 2019, pp. 2816–2824.
175	M. Kisantal, S. Sharma, T. H. Park, D. Izzo, M. M?rtens, and S. D’Amico Satellite pose estimation challenge: Dataset, competition design, and results[J]. IEEE Trans. Aerosp. Electron. Syst., 2020, 56 (5): 4083- 4098 doi: 10.1109/TAES.2020.2989063
176	S. Jahirabadkar, P. Narsay, S. Pharande, G. Deshpande, and A. Kitture, Space objects classification techniques: A survey, presented at 2020 Int. Conf. Computational Performance Evaluation (ComPE), Shillong, India, 2020, pp. 786–791.
177	D. Yadava, R. Hosangadi, S. Krishna, P. Paliwal, and A. Jain, Attitude control of a nanosatellite system using reinforcement learning and neural networks, presented at 2018 IEEE Aerospace Conf., Big Sky, MT, USA, 2018, pp. 1–8.
178	A. M. Ahmed, A. Salama, H. A. Ibrahim, M. A. E. Sayed, and S. Yacout, Prediction of battery remaining useful life on board satellites using logical analysis of data, presented at 2019 IEEE Aerospace Conf., Big Sky, MT, USA, 2019, pp. 1–8.
179	J. H. Lee, J. Park, M. Bennis, and Y. C. Ko, Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks, presented at GLOBECOM 2020–2020 IEEE Global Communications Conf., Taipei, China, 2020, pp. 1–6.
180	N. Cheng, F. Lyu, W. Quan, C. H. Zhou, H. L. He, W. S. Shi, and X. M. Shen Space/aerial-assisted computing offloading for IoT applications: A learning-based approach[J]. IEEE J. Select. Areas Commun., 2019, 37 (5): 1117- 1129 doi: 10.1109/JSAC.2019.2906789
181	C. X. Jiang and X. M. Zhu Reinforcement learning based capacity management in multi-layer satellite networks[J]. IEEE Trans. Wirel. Commun., 2020, 19 (7): 4685- 4699 doi: 10.1109/TWC.2020.2986114
182	C. Qiu, H. P. Yao, F. R. Yu, F. M. Xu, and C. L. Zhao Deep Q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks[J]. IEEE Trans. Veh. Technol., 2019, 68 (6): 5871- 5883 doi: 10.1109/TVT.2019.2907682
183	W. Y. Liu, F. Tian, Z. Y. Jiang, G. T. Li, and Q. J. Jiang, Beam-hopping based resource allocation algorithm in LEO satellite network, presented at 3^rd Int. Conf. Space Information Network, Changchun, China, 2018, pp. 113–123.
184	Z. C. Qu, G. X. Zhang, H. T. Cao, and J. D. Xie LEO satellite constellation for internet of things[J]. IEEE Access, 2017, 5: 18391- 18401 doi: 10.1109/ACCESS.2017.2735988
185	H. Tsuchida, Y. Kawamoto, N. Kato, K. Kaneko, S. Tani, S. Uchida, and H. Aruga Efficient power control for satellite-borne batteries using Q-learning in low-earth-orbit satellite constellations[J]. IEEE Wirel. Commun. Lett., 2020, 9 (6): 809- 812 doi: 10.1109/LWC.2020.2970711
186	B. K. Zhao, J. H. Liu, Z. L. Wei, and I. You A deep reinforcement learning based approach for energy efficient channel allocation in satellite internet of things[J]. IEEE Access, 2020, 8: 62197- 62206 doi: 10.1109/ACCESS.2020.2983437
187	G. F. Cui, X. Y. Li, L. X. Xu, and W. D. Wang Latency and energy optimization for MEC enhanced SAT-IoT networks[J]. IEEE Access, 2020, 8: 55915- 55926 doi: 10.1109/ACCESS.2020.2982356
188	C. C. Zhang, N. Zhang, W. Cao, K. B. Tian, and Z. Yang, An AI-based optimization of handover strategy in non-terrestrial networks, presented at 2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K), Ha Noi, Vietnam, 2020, pp. 1–6.
189	X. Q. Chen, W. Yao, Y. Zhao, X. Q. Chen, and X. H. Zheng A practical satellite layout optimization design approach based on enhanced finite-circle method[J]. Struct. Multidisc. Optim., 2018, 58 (6): 2635- 2653 doi: 10.1007/s00158-018-2042-z
190	K. Chen, J. W. Xing, S. F. Wang, and M. X. Song Heat source layout optimization in two-dimensional heat conduction using simulated annealing method[J]. Int. J. Heat Mass Transfer, 2017, 108: 210- 219 doi: 10.1016/j.ijheatmasstransfer.2016.12.007
191	Y. Aslan, J. Puskely, and A. Yarovoy Heat source layout optimization for two-dimensional heat conduction using iterative reweighted L1-norm convex minimization[J]. Int. J. Heat Mass Transfer, 2018, 122: 432- 441 doi: 10.1016/j.ijheatmasstransfer.2018.02.001
192	K. Chen, S. F. Wang, and M. X. Song Temperature-gradient-aware bionic optimization method for heat source distribution in heat conduction[J]. Int. J. Heat Mass Transfer, 2016, 100: 737- 746 doi: 10.1016/j.ijheatmasstransfer.2016.05.011
193	J. L. Sun, J. Zhang, X. Y. Zhang, and W. E. Zhou A deep learning-based method for heat source layout inverse design[J]. IEEE Access, 2020, 8: 140038- 140053 doi: 10.1109/ACCESS.2020.3013394
194	H. Li, P. Wang, C. H. Shen, and G. Y. Zhang, Show attend and read: A simple and strong baseline for irregular text recognition[J]. Proc. AAAI Conf. Artif. Intell., 2019, 33 (1): 8610- 8617
195	Y. J. Zhang and W. J. Ye Deep learning-based inverse method for layout design[J]. Struct. Multidisc. Optim., 2019, 60 (2): 527- 536 doi: 10.1007/s00158-019-02222-w
196	J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Solja?i? Nanophotonic particle simulation and inverse design using artificial neural networks[J]. Sci. Adv., 2018, 4 (6): eaar4206 doi: 10.1126/sciadv.aar4206
197	J. Tompson, K. Schlachter, P. Sprechmann, and K. Perlin, Accelerating eulerian fluid simulation with convolutional networks, in Proc. 34^th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 3424–3433.
198	A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters[J]. Integr. Mater. Manuf. Innovat., 2014, 3 (1): 90- 108 doi: 10.1186/2193-9772-3-8
199	P. Robustillo, J. Zapata, J. A. Encinar, and J. Rubio ANN characterization of multi-layer reflectarray elements for contoured-beam space antennas in the Ku-band[J]. IEEE Trans. Antennas Propag., 2012, 60 (7): 3205- 3214 doi: 10.1109/TAP.2012.2196941
200	A. Freni, M. Mussetta, and P. Pirinoli Neural network characterization of reflectarray antennas[J]. Int. J. Antennas Propagat., 2012, 2012: 541354
201	F. Güne?, S. Nesil, and S. Demirel Design and analysis of minkowski reflectarray antenna using 3-D CST microwave studio-based neural network model with particle swarm optimization[J]. Int. J. RF Microw. Comput.-Aided Eng., 2013, 23 (2): 272- 284
202	P. Robustillo, J. Zapata, J. A. Encinar, and M. Arrebola Design of a contoured-beam reflectarray for a EuTELSAT European coverage using a stacked-patch element characterized by an artificial neural network[J]. IEEE Antennas Wirel. Propag. Lett., 2012, 11: 977- 980 doi: 10.1109/LAWP.2012.2213567
203	T. Shan, M. K. Li, S. S. Xu, and F. Yang, Synthesis of refiectarray based on deep learning technique, presented at 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conf. (CSQRWC), Xuzhou, China, 2018, pp. 1–2.
204	M. Salucci, L. Tenuti, G. Oliveri, and A. Massa Efficient prediction of the EM response of reflectarray antenna elements by an advanced statistical learning method[J]. IEEE Trans. Antennas Propag., 2018, 66 (8): 3995- 4007 doi: 10.1109/TAP.2018.2835566
205	D. R. Prado, J. A. López-Fernández, G. Barquero, M. Arrebola, and F. Las-Heras Fast and accurate modeling of dual-polarized reflectarray unit cells using support vector machines[J]. IEEE Trans. Antennas Propag., 2018, 66 (3): 1258- 1270 doi: 10.1109/TAP.2018.2790044
206	D. R. Prado, J. A. López-Fernández, M. Arrebola, and G. Goussetis Support vector regression to accelerate design and crosspolar optimization of shaped-beam reflectarray antennas for space applications[J]. IEEE Trans. Antennas Propag., 2019, 67 (3): 1659- 1668 doi: 10.1109/TAP.2018.2889029
207	D. R. Prado, J. A. López-Fernández, M. Arrebola, M. R. Pino, and G. Goussetis Wideband shaped-beam reflectarray design using support vector regression analysis[J]. IEEE Antennas Wirel. Propag. Lett., 2019, 18 (11): 2287- 2291 doi: 10.1109/LAWP.2019.2932902
208	P. Henttu and S. Aromaa, Consecutive mean excision algorithm, presented at IEEE 7^th Int. Symp. Spread Spectrum Techniques and Applications, Prague, Czech Republic, 2002, pp. 450–454.
209	H. Saarnisaari, Consecutive mean excision algorithms in narrowband or short time interference mitigation, presented at PLANS 2004. Position Location and Navigation Symp. (IEEE Cat. No.04CH37556), Monterey, CA, USA, 2004, pp. 447–454.
210	H. Saarnisaari and P. Henttu, Impulse detection and rejection methods for radio systems, presented at IEEE Military Communications Conf., 2003. MILCOM, Boston, MA, USA, 2003, pp. 1126–1131.
211	H. G. Keane, A new approach to frequency line tracking, presented at Conf. Record of the 25^th Asilomar Conf. Signals, Systems & Computers, Pacific Grove, CA, USA, 1991, pp. 808–812.
212	R. Escbbach, Z. Fan, K. T. Knox, and G. Marcu Threshold modulation and stability in error diffusion[J]. IEEE Signal Process. Mag., 2003, 20 (4): 39- 50 doi: 10.1109/MSP.2003.1215230
213	H. Mustafa, M. Doroslovacki, and H. Deng, Algorithms for emitter detection based on the shape of power spectrum, in Proc. of the Conf. on Information Sciences and Systems, Baltimore, MD, USA, 2003, pp. 808–812.
214	J. Vartiainen, Localization of multiple narrowband signals based on the FCME algorithm, in Proc. Nordic Radio Symp., Oulu, Finland, 2004, p. 5.
215	J. Vartiainen, J. Lehtomaki, and H. Saarnisaari, Double-threshold based narrowband signal extraction, presented at 2005 IEEE 61^st Vehicular Technology Conf., Stockholm, Sweden, 2005, pp. 1288–1292.
216	J. Kim, M. Kim, I. Won, S. Yang, K. Lee, and W. Huh, A biomedical signal segmentation algorithm for event detection based on slope tracing, presented at 2009 Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 2009, pp. 1889–1892.
217	O. Morozov and P. Ovchinnikov, Neural network detection of MSK signals, presented at 2009 IEEE 13^th Digital Signal Processing Workshop and 5^th IEEE Signal Processing Education Workshop, Marco Island, FL, USA, 2009, pp. 594–596.
218	Y. Yuan, Z. H. Sun, Z. H. Wei, and K. B. Jia DeepMorse: A deep convolutional learning method for blind morse signal detection in wideband wireless spectrum[J]. IEEE Access, 2019, 7: 80577- 80587 doi: 10.1109/ACCESS.2019.2923084
219	H. Huang, J. Q. Li, J. Wang, and H. Wang FCN-based carrier signal detection in broadband power spectrum[J]. IEEE Access, 2020, 8: 113042- 113051 doi: 10.1109/ACCESS.2020.3003683
220	J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, in Proc. 2015 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 3431–3440.
221	K. M. He, G. Gkioxari, P. Dollár, and R. Girshick, Mask R-CNN, in Proc. 2017 IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 2980–2988.
222	University of Freiburg, U-Net: Convolutional networks for biomedical image segmentation, https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/,?2015.

No related articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed