참고문헌 (47)

1. T. Wang et al., "Deep Learning for wireless Physical Layer: Opportunities and Challenges," China Commun., vol. 14, no. 11, Nov. 2017, pp. 92-111. 

2. C. Wang, P. Patras, and H. Haddadi, "Deep Learning in Mobile and Wireless Networking: A Survey," eprint arXiv: 1803.04311, Mar. 2018. 

3. Q. Mao, F. Hu, and Q. Hao, "Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey," IEEE Commun. Surveys Tutorials, Early Access, June 2018. 

4. M. Chen et al., "Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks," eprint arXiv: 1710.02913, Oct. 2017. 

5. X. You et al., "AI for 5G: Research Directions and Paradigms," eprint arXiv: 1807.08671, July 2018. 

6. Z. Qin et al., "Deep Learning in Physical Layer Communications," eprint arXiv: 1807.11713, July 2018. 

7. T.M. Mitchell, Machine Learning, McGraw-Hill: Boston, MA, USA, 1997. 

8. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction (2nd Edition), MIT Press: Cambridge, MA, USA, 2018. 

9. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer: New York, USA, 2013. 

10. G. James et al., An Introduction to Statistical Learning: with Applicationsin R, Springer: New York, USA, 2014. 

11. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer: New York, USA, 2016. 

12. G. Cybenko, "Approximation by Superpositions of a Sigmoidal Function," Math. Contr., Signals, Syst., vol. 2, no. 4, Dec. 1989, pp. 303-314. 

13. K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Netw., vol. 4, 1991, pp. 251-257. 

    상세보기

14. I. Goodfellow, Y. Bengio, and A. Kourville, Deep Learning, MIT Press: Cambridge, MA, USA, 2016. 

15. T.J. O'Shea and J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," IEEE Trans. Cognitive Commun. Netw., vol. 3, no. 4, 2017, pp. 563-575. 

    상세보기

16. DEEPSIG DATASET: RADIOML 2016. 10A. https://www.deepsig.io/datasets/ 

17. D. George and E.A. Huerta, "Deep Neural Networks to Enable Real-Time Multimessenger Astrophysics," Phys. Rev. D, vol. 97, no. 4, 2018, Article no. 044039. 

    상세보기

18. A. Hirose, Complex-Valued Neural Networks, Springer Science & Business Media: Berlin, Germany, 2012. 

19. M.F. Amin et al., "Wirtinger Calculus Based Gradient Descent and Levenberg-Marquardt Learning Algorithms in Complex-Valued Neural Networks," In B.L. Lu, L. Zhang, and J. Kwok (eds), Lecture Notes in Computer Science, vol 7062, Springer: Berlin, Heidelberg, 2011. 

20. T. O'Shea, T. Erpek, and T. C. Clancy, "Deep Learning Based MIMO Communications," arXiv preprint arXiv: 1707.07980, 2017. 

21. S. Drner et al., "Deep Learning-Based Communication Over the Air," Asilomar Conf. Signals, Syst. Comput., vol. 12, no. 1, Oct. 2017, pp. 1791-1795. 

22. T.J. O'Shea, K. Karra, and T. C. Clancy, "Learning to Communicate: Channel Auto-Encoders Domain Specific Regularizers and Attention," in IEEE Int. Symp. Signal Process. Inform. Technol., Limassol. Cyprus, Dec. 12-14 2016, pp. 223-228. 

23. E. Nachmani et al., "Deep Learning Methods for Improved Decoding of Linear Codes," IEEE J. Sel. Topics Signal Process., vol. 12, no. 1, 2018, pp. 119-131. 

    상세보기

24. S. Hemati and A.H. Banihashemi, "Dynamics and Performance Analysis of Analog Iterative Decoding for Low-Density Parity-Check (LDPC) Codes," IEEE Trans. Commun., vol. 54, no. 1, Jan. 2006, pp. 61-70. 

    상세보기

25. F. Liang, C. Shen, and F. Wu, "An Iterative BP-CNN Architecture for Channel Decoding," IEEE J. Sel. Topics Signal Process., vol. 12, no. 1, 2018, pp. 144-159. 

26. E. Nachmani et al., "RNN Decoding of Linear Block Codes," arXiv preprint arXiv: 1702.07560, 2017 

27. W. Lyu et al., "Performance Evaluation of Channel Decoding With Deep Neural Networks," in IEEE Int. Conf. Commun. (ICC), Kansas, MO, USA, May 20-24, 2018, pp. 1-6. 

28. N. Samuel, T. Diskin, and A. Wiesel, "Deep MIMO Detection," in IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC), Sapporo, Japan, July 3-6, 2017, pp. 690-694. 

29. X. Yan et al., "Signal Detection of MIMO-OFDM System Based on Auto Encoder and Extreme Learning Machine," in Int. Joint Conf. Neural Netw. (IJCNN), Anchorage, AK, USA, May 14-19, 2017, pp. 1602-1606. 

30. Y.-S. Jeon, S.-N. Hong, and N. Lee, "Blind Detection for MIMO Systems with Low-Resolution ADCs Using Supervised Learning," Proc. IEEE Int. Conf. Commun. (ICC), Paris, France, May 21-25, 2017, pp. 1-6. 

31. M. Imanishi, S. Takabe, and T. Wadayama, "Deep Learning-Aided Iterative Detector for Massive Overloaded MIMO Channels," arXiv preprint arXiv: 1806.10827, 2018. 

32. X. Jin and H. Kim, "Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels," arXiv preprint arXiv: 1807.09571, 2018. 

33. N. Farsad and A. Goldsmith, "Detection Algorithms for Communication Systems Using Deep Learning," arXiv preprint arXiv: 1705.08044, 2017. 

34. H. Ye, G.Y. Li, and B.-H.F. Juang, "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems," IEEE Wireless Commun. Lett., vol. 7, no. 1, Feb. 2018, pp. 114-117. 

    상세보기

35. D. Neumann, W. Utschick, and T. Wiese, "Deep Channel Estimation," in WSA, Berlin, Germany, Mar. 15-17, 2017, pp. 1-6. 

36. C.-K. Wen, W.-T. Shih, and S. Jin, "Deep Learning for Massive MIMO CSI Feedback," IEEE Wireless Commun. Lett., Early Access, 2018. 

37. H. Nikopour and H. Baligh, "Sparse Code Multiple Access," in IEEE Annu. Int. Symp. Personal, Indoor, Mobile Radio Commun. (PIMRC), London, UK, Sept. 8-11, 2013, pp. 332-336. 

38. M. Taherzadeh et al., "SCMA Codebook Design," in IEEE Veh. Technol. Conf. (VTC2014-Fall), Vancouver, Canada, Sept. 14-17, 2014, pp. 1-5. 

39. Y. Wu et al., "Sparse Code Multiple Access for 5G Radio Transmission," in IEEE Veh. Technol. Conf. (VTC-Fall), Toronto, Canada, Sept. 24-27, 2017, pp. 1-6. 

40. M. Kim et al., "Deep Learning-Aided SCMA," IEEE Commun. Lett., vol. 22, no. 4, Apr. 2018, pp. 720-723. 

    상세보기

41. Y. Saito et al., "Non-Orthogonal Multiple Access (NOMA) for Cellular Future Radio Access," IEEE Veh. Technol. Conf. (VTC Spring), Dresden, Germany, June 2-5, 2013, pp. 1-5. 

42. L. Dai et al., "Non-Orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends," IEEE Commun. Mag., vol. 53, no. 9, Sept. 2015, pp. 74-81. 

    상세보기

43. G. Gui et al., "Deep Learning for An Effective Non-Orthogonal Multiple Access Scheme," in IEEE Trans. Veh. Technol., Early Access, 2018. 

44. H. Sun et al., "Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management," eprint arXiv: 1705.09412, Oct. 2017. 

45. W. Lee, M. Kim, and D. Cho, "Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network," IEEE Commun. Lett., vol. 22, no. 6, June 2018, pp. 1276-1279. 

    상세보기

46. F. Liang et al., "Towards Optimal Power Control via Ensembling Deep Neural Networks," eprint arXiv: 1807.10025, July 2018. 

47. M. A. Wijaya, K. Fukawa and H. Suzuki, "Intercell-Interference Cancellation and Neural Network Transmit Power Optimization for MIMO Channels," in IEEE Veh. Technol. Conf. (VTC2015-Fall), Boston, MA, USA, Sept. 6-9, 2015, pp. 1-5