[1] [1] WANG Changhong, ZHANG Zhongshan, WU Jiayi, et al. An overview of protected satellite communications in intelligent age[J]. SCIENCE CHINA Information Sciences, 2021, 64(6): 161301. DOI: 10.1007/s11432-019-2928-9.

[2] [2] WOLF B J, HUANG J C. Implementation and testing of the Protected Tactical Waveform(PTW)[C]//IEEE Military Communications Conference. Tampa, FL, USA: IEEE, 2015: 181-186.

[4] [4] FOURATI F, ALOUINI M S. Artificial intelligence for satellite communication: a review[J]. IEEE Journal on Selected Areas in Communications, 2021, 2(3): 213-243. DOI: 10.23919/ICN.2021.0015.

[6] [6] UJAN S, NAVIDI N, LANDRY R J. Hierarchical classification method for radio frequency interference recognition and characterization in satcom[J]. Applied Sciences, 2020, 10(13): 4608. DOI: 10.3390/app10134608.

[7] [7] SHEN Junren, LI Yusheng, ZHU Yonggang, et al. Cooperative multi-node jamming recognition method based on deep residual network[J]. Electronics Letters, 2022, 11(20): 11203280. DOI: 10.3390/electronics11203280.

[8] [8] SADEGHI M, LARSSON E G. Adversarial attacks on deep-learning based radio signal classification[J]. IEEE Wireless Communications Letters, 2019, 8(1): 213-216. DOI: 10.1109/LWC.2018.2867459.

[9] [9] WEI Peng, WANG Shilian, LUO Junshan, et al. Optimal frequency-hopping anti-jamming strategy based on multi-step prediction Markov decision process[J]. Wireless Networks, 2021, 27(7): 4581-4601. DOI: 10.1007/s11276-021-02735-7.

[12] [12] SONG Bailin, XU Hua, JIANG Lei, et al. An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning[J]. Journal of Northwestern Polytechnical University, 2021, 39(3): 641-649. DOI: 10.1051/jnwpu/20213930641.

[13] [13] WEI Peng, WANG Shilian, LUO Junshan. Adaptive modem and interference suppression based on deep learning[J]. Transactions on Emerging Telecommunications Technologies, 2021, 32(4): e4220. DOI: 10.1002/ett.4220.

[14] [14] WEI Peng, LU Ruimin, YE Ganhua, et al. Channel synchronization based on deep learning[J]. Transactions on Emerging Telecommunications Technologies, 2022, 34(1): e4656. DOI: 10.1002/ett.4656.

[15] [15] O'SHEA T J, CORGAN J, CLANCY T C. Unsupervised representation learning of structured radio communication signals[C]//2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines(SPLINE). Aalborg, Denmark: IEEE, 2016: 1-5. DOI: 10.1109/SPLIM.2016.7528397.

[16] [16] O'SHEA T J, KARRA K, CLANCY T C. Learning to communicate: channel auto-encoders, domain specific regularizers, and attention[C]//2016 IEEE International Symposium on Signal Processing and Information Technology(ISSPIT). Limassol, Cyprus: IEEE, 2016: 223-228. DOI: 10.1109/ISSPIT.2016.7886039.

[17] [17] O'SHEA T J, HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4): 563-575. DOI: 10.1109/TCCN.2017.2758370.

[18] [18] O'SHEA T J, ROY T, WEST N, et al. Physical layer communications system design over-the-air using adversarial networks[C]//2018 the 26th European Signal Processing Conference(EUSIPCO). Rome, Italy: IEEE, 2018: 529-532. DOI: 10.23919/EUSIPCO.2018.8553233.

[19] [19] YE H, LI G Y, JUANG B H F, et al. Channel agnostic end-to-end learning based communication systems with conditional GAN[C]//2018 IEEE Globecom Workshops(GC Wkshps). Abu Dhabi, United Arab Emirates: IEEE, 2018: 1-5. DOI: 10.1109/GLOCOMW.2018.8644250.

[20] [20] WEI Peng, LU Ruimin, WANG Shilian, et al. Multi-modem implementation method based on deep autoencoder network[C]//Wireless and Satellite Systems: Springer International Publishing. Nanjing, China: Springer Link, 2021: 487-501. DOI: 10.1007/978-3-030-69072-4_40.