Haberleşme Sistemlerinde Derin Öğrenme

Öz

Makine öğreniminde derin öğrenme en başarılı öğrenme yöntemi olmuştur. Derin öğrenme özellikle veri miktarının çok olduğu durumlarda diğer makine öğrenimi yöntemlerine açık ara üstünlük sağlarken, verinin az olduğu durumlarda diğer makine öğrenim yöntemlerine yakın bir sonuç üretebilmektedir. Bu yeni öğrenme yöntemi haberleşme teknolojilerinde kullanılan fiziksel katmanların yeniden tasarlanmasından telsiz ağların modellenmesine kadar birçok yeniliğe katkı sunacak potansiyele sahiptir. Özellikle matematiksel modellemesi zor olan haberleşme sistemlerinde, örneğin 5G ve moleküler haberleşme, kolaylık sağlamaktadır. Bundan dolayı derin öğrenmenin haberleşme sistemlerininde uygulanmasını konu alan birçok araştırma son zamanlarda yapılmaktadır. Buna rağmen haberleşme teknolojileriyle ilgili kurum ve araştırmacıların derin öğrenme yöntemlerine olan uzaklığı bu çalışmaların sayısını ve etkisini sınırlı bırakmıştır. Bu sebeple derin öğrenmenin haberleşme teknolojilerine uygulamasını konu alan çalışmaların toplu olarak incelenmesi, elde edilen başarıların değerlendirilmesi, yapabilecek yeni araştırma konularının belirlenmesine katkı sunacak çalışmalara gerek duyulmaktadır. Bu amaca yönelik olarak bu çalışmada öncelikle derin öğrenme yöntemi, başarıları ve kullanım alanları özetle sunuldu ve haberleşme teknolojilerinin gelişmesine katkı sunan çalışmalar sınıflandırılarak karşılaştırmalı incelendi. Derin öğrenmenin haberleşmede daha başarılı kullanımı için yapılması gerekenler tartışıldı ve yeni nesil haberleşme sistemlerine öncülük edebilecek derin öğrenme tabanlı araştırma alanları belirlendi.

Anahtar Kelimeler

• Akıllı Haberleşme,Haberleşme,Derin öğrenme,Makine Öğrenimi,Telsiz Haberleşme

Deep Learning for Communication Systems

Abstract

Deep learning has become the most successful learning method in machine learning. While deep learning provides a clear advantage over other machine learning methods, especially when the amount of data is high, it can produce an approximate result to other machine teaching methods when data is low. This new learning method has the potential to contribute to many innovations, from redesigning the physical layers used in communication technologies to modeling wireless networks. It is particularly useful in communication systems where mathematical modeling is difficult, for example, 5G and molecular communication. Therefore, many types of research on the application of deep learning in communication systems have been conducted recently. However, the distance of institutions and researchers about communication technologies to deep learning methods has limited the number and impact of these studies. Therefore, it is necessary to collectively examine the studies that involve the application of deep learning to communication technologies, to evaluate the achievements, and to contribute to the determination of new research topics. For this purpose, in this study, firstly, the achievements of deep learning and usage areas are summarized and then the studies that contribute to the development of communication technologies are classified and examined comparatively. To make deep learning more effective in communication, what needs to be done were discussed and deep learning-based research areas that could lead to next-generation communication systems were determined.

Keywords

• Communication,Deep Learning,Machine Learning,Intelligent Communication

References

1. Ahmed, K. I., Tabassum, H., & Hossain, E. (2018). Deep Learning for Radio Resource Allocation in Multi-Cell Networks. CoRR, abs/1808.00667. Retrieved from http://arxiv.org/abs/1808.00667
2. Al-Baidhani, A., & Fan, H. H. (2019). Learning for Detection: A Deep Learning Wireless Communication Receiver Over Rayleigh Fading Channels. 2019 International Conference on Computing, Networking and Communications (ICNC) (pp. 6–10). doi:10.1109/ICCNC.2019.8685517
3. Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., et al. (2016). Deep speech 2: End-to-end speech recognition in english and mandarin. International conference on machine learning (pp. 173–182).
4. Andrychowicz, M., Denil, M., Colmenarejo, S. G., Hoffman, M. W., Pfau, D., Schaul, T., & Freitas, N. de. (2016). Learning to learn by gradient descent by gradient descent. CoRR, abs/1606.04474. Retrieved from http://arxiv.org/abs/1606.04474
5. Arnold, M., Dörner, S., Cammerer, S., Yan, S., Hoydis, J., & Brink, S. ten. (2019). Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction. CoRR, abs/1901.03664.
6. Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization. arXiv preprint arXiv:1607.06450. Bourtsoulatze, E., Kurka, D. B., & Gunduz, D. (2018). Deep joint source-channel coding for wireless image transmission. arXiv preprint arXiv:1809.01733.
7. Chen, D., & Laneman, J. N. (2006). Modulation and demodulation for cooperative diversity in wireless systems. IEEE Transactions on Wireless Communications, 5(7), 1785–1794.
8. Cheng, X., Liu, D., Wang, C., Yan, S., & Zhu, Z. (2019). Deep Learning based Channel Estimation and Equalization Scheme for FBMC/OQAM Systems. IEEE Wireless Communications Letters, 1–1. doi:10.1109/LWC.2019.2898437

9. Chikha, W. B., Dayoub, I., Hamouda, W., & Attia, R. (2014). Modulation Recognition for MIMO Relaying Broadcast Channels with Direct Link. IEEE Wireless Communications Letters, 3(1), 50–53. doi:10.1109/WCL.2013.111113.130655
10. Corlay, V., Boutros, J. J., Ciblat, P., & Brunel, L. (2018). Multilevel MIMO Detection with Deep Learning. 2018 52nd Asilomar Conference on Signals, Systems, and Computers (pp. 1805–1809). doi:10.1109/ACSSC.2018.8645519
11. Diamandis, T. (2017). Survey on Deep Learning Techniques for Wireless Communications.
12. Ding, Z., Lei, X., Karagiannidis, G. K., Schober, R., Yuan, J., & Bhargava, V. K. (2017). A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends. IEEE Journal on Selected Areas in Communications, 35(10), 2181–2195.
13. Dörner, S., Cammerer, S., Hoydis, J., & Brink, S. ten. (2018). Deep learning based communication over the air. IEEE Journal of Selected Topics in Signal Processing, 12(1), 132–143.
14. Eisen, M., Zhang, C., Chamon, L. F. O., Lee, D. D., & Ribeiro, A. (2018). Online Deep Learning in Wireless Communication Systems. 2018 52nd Asilomar Conference on Signals, Systems, and Computers (pp. 1289–1293). doi:10.1109/ACSSC.2018.8645312
15. Elbaz, D., & Zibulevsky, M. (2018). End to End Deep Neural Network Frequency Demodulation of Speech Signals. Future of Information and Communication Conference (pp. 1–11). Springer.
16. Farsad, N., & Goldsmith, A. (2018). Neural network detection of data sequences in communication systems. arXiv preprint arXiv:1802.02046.
17. Felix, A., Cammerer, S., Dörner, S., Hoydis, J., & Brink, S. ten. (2018). OFDM-Autoencoder for End-to-End Learning of Communications Systems. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 1–5.
18. Fujihashi, T., Koike-Akino, T., Watanabe, T., & Orlik, P. V. (2018). Nonlinear Equalization with Deep Learning for Multi-Purpose Visual MIMO Communications. 2018 IEEE International Conference on Communications (ICC) (pp. 1–6). doi:10.1109/ICC.2018.8422544
19. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). MIT press Cambridge.
20. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., et al. (2014). Generative adversarial nets. Advances in neural information processing systems (pp. 2672–2680).
21. Graves, A., & Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent neural networks. International Conference on Machine Learning (pp. 1764–1772).
22. Gruber, T., Cammerer, S., Hoydis, J., & Brink, S. t. (2017). On deep learning-based channel decoding. 2017 51st Annual Conference on Information Sciences and Systems (CISS) (pp. 1–6). doi:10.1109/CISS.2017.7926071
23. Guo, C., Liang, L., & Li, G. Y. (2019). Resource Allocation for V2X Communications: A Large Deviation Theory Perspective. IEEE Wireless Communications Letters, 1–1. doi:10.1109/LWC.2019.2908165
24. Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. CoRR, abs/1510.00149.
25. He, H., Wen, C., Jin, S., & Li, G. Y. (2018a). A Model-Driven Deep Learning Network for MIMO Detection. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 584–588). doi:10.1109/GlobalSIP.2018.8646357
26. He, H., Wen, C., Jin, S., & Li, G. Y. (2018b). Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems. IEEE Wireless Communications Letters, 7(5), 852–855. doi:10.1109/LWC.2018.2832128
27. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine, 29.
28. Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527–1554.
29. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504–507.
30. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
31. Ibnkahla, M. (2000). Applications of neural networks to digital communications–a survey. Signal processing, 80(7), 1185–1215. Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.
32. Jaderberg, M., Czarnecki, W. M., Osindero, S., Vinyals, O., Graves, A., Silver, D., & Kavukcuoglu, K. (2017). Decoupled neural interfaces using synthetic gradients. Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 1627–1635). JMLR. org.
33. Jia, Z., Cheng, W., & Zhang, H. (2019). A Partial Learning Based Detection Scheme for Massive MIMO. IEEE Wireless Communications Letters, 1–1. doi:10.1109/lwc.2019.2909019
34. Jiang, Z., Chen, S., Molisch, A. F., Vannithamby, R., Zhou, S., & Niu, Z. (2019). Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach. IEEE Communications Magazine, 57, 28–34.
35. Jiang, Z., He, Z., Chen, S., Molisch, A. F., Zhou, S., & Niu, Z. (2018). Inferring Remote Channel State Information: Cramér-Rae Lower Bound and Deep Learning Implementation. 2018 IEEE Global Communications Conference (GLOBECOM), 1–7.
36. Kang, J., Chun, C., & Kim, I. (2018). Deep-Learning-Based Channel Estimation for Wireless Energy Transfer. IEEE Communications Letters, 22(11), 2310–2313. doi:10.1109/LCOMM.2018.2871442
37. Karanov, B., Lavery, D., Bayvel, P., & Schmalen, L. (2019). End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks. CoRR, abs/1901.08570.
38. Kim, K., Lee, J., & Choi, J. (2018). Deep Learning Based Pilot Allocation Scheme (DL-PAS) for 5G Massive MIMO System. IEEE Communications Letters, 22(4), 828–831. doi:10.1109/LCOMM.2018.2803054
39. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
40. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097–1105).
41. Laura Brink Anant Sahai, E. Ed. John Wawrzynek. (2018). Deep Networks for Equalization in Communications (No. UCB/EECS-2018-177). Electrical Engineering and Computer Sciences University of California at Berkeley. Retrieved from https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-177.pdf
42. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
43. Lee, J. H., Kim, J., Kim, B., Yoon, D., & Choi, J. W. (2017). Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network. Entropy, 19, 454.
44. Lee, W., Kim, M., & Cho, D. (2018). Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network. IEEE Communications Letters, 22(6), 1276–1279. doi:10.1109/LCOMM.2018.2825444
45. Li, H., Gao, H., Lv, T., & Lu, Y. (2018). Deep Q-Learning Based Dynamic Resource Allocation for Self-Powered Ultra-Dense Networks. 2018 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1–6). doi:10.1109/ICCW.2018.8403505
46. Li, J., Gao, H., Lv, T., & Lu, Y. (2018). Deep reinforcement learning based computation offloading and resource allocation for MEC. 2018 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6). doi:10.1109/WCNC.2018.8377343
47. Li, X., Alkhateeb, A., & Tepedelenlioglu, C. (2018). Generative adversarial estimation of channel covariance in vehicular millimeter wave systems. 2018 52nd Asilomar Conference on Signals, Systems, and Computers (pp. 1572–1576). IEEE.
48. Lin, T., & Zhu, Y. (2019). Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning.
49. Liu, X., Yang, D., & Gamal, A. E. (2017). Deep neural network architectures for modulation classification. 2017 51st Asilomar Conference on Signals, Systems, and Computers, 915–919.
50. Mendis, G. J., Wei, J., & Madanayake, A. (2016). Deep learning-based automated modulation classification for cognitive radio. 2016 IEEE International Conference on Communication Systems (ICCS) (pp. 1–6). doi:10.1109/ICCS.2016.7833571
51. Mendis, G. J., Wei, J., & Madanayake, A. (2019). Deep Learning based Radio-Signal Identification with Hardware Design. IEEE Transactions on Aerospace and Electronic Systems, 1–1. doi:10.1109/TAES.2019.2891155
52. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.
53. Mohammad, A. S., Reddy, N., James, F., & Beard, C. (2018). Demodulation of faded wireless signals using deep convolutional neural networks. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 969–975). IEEE.
54. Mohammad, U., & Sorour, S. (2018). Adaptive Task Allocation for Mobile Edge Learning. arXiv preprint arXiv:1811.03748.
55. Nachmani, E., Be’ery, Y., & Burshtein, D. (2016). Learning to decode linear codes using deep learning. 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 341–346). doi:10.1109/ALLERTON.2016.7852251
56. Nachmani, E., Marciano, E., Lugosch, L., Gross, W. J., Burshtein, D., & Be’ery, Y. (2018). Deep Learning Methods for Improved Decoding of Linear Codes. IEEE Journal of Selected Topics in Signal Processing, 12(1), 119–131. doi:10.1109/JSTSP.2017.2788405
57. Nasir, Y. S., & Guo, D. (2018). Deep reinforcement learning for distributed dynamic power allocation in wireless networks. arXiv preprint arXiv:1808.00490.
58. O’Shea, T. J., & Corgan, J. (2016). Convolutional Radio Modulation Recognition Networks. CoRR, abs/1602.04105. Retrieved from http://arxiv.org/abs/1602.04105
59. O’Shea, T. J., Corgan, J., & Clancy, T. C. (n.d.). Unsupervised Representation Learning of Structured Radio Communication Signals.
60. O’Shea, T. J., Erpek, T., & Clancy, T. C. (2017). Deep learning based MIMO communications. arXiv preprint arXiv:1707.07980.
61. O’Shea, T. J., Erpek, T., & Clancy, T. C. (n.d.). Deep Learning Based MIMO Communications.
62. O’Shea, T. J., & Hoydis, J. (2017). An introduction to machine learning communications systems. arXiv preprint, 1702.
63. O’Shea, T. J., Karra, K., & Clancy, T. C. (2016). Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention. Signal Processing and Information Technology (ISSPIT), 2016 IEEE International Symposium on (pp. 223–228). IEEE.
64. O’Shea, T. J., Roy, T., & Clancy, T. C. (2017). Over the Air Deep Learning Based Radio Signal Classification. CoRR, abs/1712.04578. Retrieved from http://arxiv.org/abs/1712.04578
65. O’Shea, T. J., Roy, T., & West, N. (2018). Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks. CoRR, abs/1805.06350.
66. O’Shea, T. J., Roy, T., West, N., & Hilburn, B. C. (2018). Physical Layer Communications System Design Over-the-Air Using Adversarial Networks. arXiv preprint arXiv:1803.03145.
67. O’Shea, T., Karra, K., & Clancy, T. C. (2017). Learning approximate neural estimators for wireless channel state information. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–7). IEEE.
68. Ramjee, S., Ju, S., Yang, D., Liu, X., Gamal, A. E., & Eldar, Y. C. (2019). Fast Deep Learning for Automatic Modulation Classification. CoRR, abs/1901.05850.
69. Reddy, Y. B. (2006). Reinforcement Learning for Resource Allocation in Multiuser OFDM Systems. ICWN (pp. 78–83). Sak, H., Senior, A., Rao, K., & Beaufays, F. (2015). Fast and accurate recurrent neural network acoustic models for speech recognition. arXiv preprint arXiv:1507.06947.
70. Samuel, N., Diskin, T., & Wiesel, A. (2017). Deep MIMO detection. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1–5). doi:10.1109/SPAWC.2017.8227772
71. Sanguinetti, L., Zappone, A., & Debbah, M. (2018). Deep Learning Power Allocation in Massive MIMO. 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1257–1261.
72. Saud Mobark Aldossari, K.-C. C. (2019). Machine Learning for Wireless Communication Channel Modeling: An Overview. Wireless Personal Communications, 1.
73. Shen, Y., Shi, Y., Zhang, J., & Letaief, K. B. (2018). LORA: Learning to Optimize for Resource Allocation in Wireless Networks with Few Training Samples. arXiv preprint arXiv:1812.07998.
74. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Driessche, G. van den, Schrittwieser, J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484–489.
75. Soltani, M., Pourahmadi, V., Mirzaei, A., & Sheikhzadeh, H. (2019). Deep Learning-Based Channel Estimation. IEEE Communications Letters.
76. Sorokina, M., & Turitsyn, S. (2014). Regeneration limit of classical Shannon capacity. Nature communications, 5, 3861.
77. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
78. Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017). Learning to optimize: Training deep neural networks for wireless resource management. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1–6). doi:10.1109/SPAWC.2017.8227766
79. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., et al. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
80. Vaezi, M., Schober, R., Ding, Z., & Poor, H. V. (2018). Non-orthogonal multiple access: Common myths and critical questions. arXiv preprint arXiv:1809.07224.
81. Wang, H., Wu, Z., Ma, S., Lu, S., Zhang, H., Ding, G., & Li, S. (2019). Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics. IEEE Access, 7, 30792–30801.
82. Wang, J., Zhao, L., Liu, J., & Kato, N. (2019). Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach. IEEE Transactions on Emerging Topics in Computing, 1–1. doi:10.1109/TETC.2019.2902661
83. West, N. E., & O’Shea, T. J. (2017). Deep Architectures for Modulation Recognition. CoRR, abs/1703.09197. Retrieved from http://arxiv.org/abs/1703.09197
84. Wu, C., Zhang, Li, Q., Fu, Z., Zhu, W., & Zhang, Y. (2019). Enabling Flexible Resource Allocation in Mobile Deep Learning Systems. IEEE Transactions on Parallel and Distributed Systems, 30(2), 346–360. doi:10.1109/TPDS.2018.2865359
85. Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., et al. (2016). Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256.
86. Xu, S., Liu, P., Wang, R., & Panwar, S. S. (2018). Realtime Scheduling and Power Allocation Using Deep Neural Networks. CoRR, abs/1811.07416.
87. Xu, W., Wu, Z., Ueng, Y., You, X., & Zhang, C. (2017). Improved polar decoder based on deep learning. 2017 IEEE International Workshop on Signal Processing Systems (SiPS) (pp. 1–6). doi:10.1109/SiPS.2017.8109997
88. Xu, Z., Wang, Y., Tang, J., Wang, J., & Gursoy, M. C. (2017). A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs. Communications (ICC), 2017 IEEE International Conference on (pp. 1–6). IEEE.
89. Yang, T., ű, Y., Gursoy, M. C., Schmeink, A., & Mathar, R. (2018). Deep Reinforcement Learning based Resource Allocation in Low Latency Edge Computing Networks. 2018 15th International Symposium on Wireless Communication Systems (ISWCS) (pp. 1–5). doi:10.1109/ISWCS.2018.8491089
90. Yang, Y., Gao, F., Ma, X., & Zhang, S. (2019). Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels. IEEE Access, 7, 36579–36589. doi:10.1109/ACCESS.2019.2901066
91. Yang, Y., Li, Y., Zhang, W., Qin, F., Zhu, P., & Wang, C. (2019). Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities. IEEE Communications Magazine, 57(3), 22–27. doi:10.1109/MCOM.2019.1800635
92. Yashashwi, K., Sethi, A., & Chaporkar, P. (2019). A Learnable Distortion Correction Module for Modulation Recognition. IEEE Wireless Communications Letters, 8(1), 77–80. doi:10.1109/LWC.2018.2855749
93. Ye, H., & Li, G. Y. (2017). Initial Results on Deep Learning for Joint Channel Equalization and Decoding. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) (pp. 1–5). doi:10.1109/VTCFall.2017.8288419
94. Ye, H., Li, G. Y., & Juang, B.-H. (2018). Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters, 7(1), 114–117.
95. Ye, H., Li, Y. G., & Juang, B. F. (2019). Deep Reinforcement Learning for Resource Allocation in V2V Communications. IEEE Transactions on Vehicular Technology, 1–1. doi:10.1109/TVT.2019.2897134
96. Zhang, D., Ding, W., Zhang, B., Xie, C., Li, H., Liu, C., & Han, J. (2018). Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles. Sensors.
97. Zhao, N., Liang, Y., Niyato, D., Pei, Y., & Jiang, Y. (2018). Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Networks. 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). doi:10.1109/GLOCOM.2018.8647611
98. Zhou, Y., Fadlullah, Z. M., Mao, B., & Kato, N. (2018). A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks. IEEE Network, 32(6), 28–34.