전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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AI 기반 이동통신 물리계층 기술 동향과 전망

Physical-Layer Technology Trend and Prospect for AI-based Mobile Communication

  • 발행 : 2020.10.01
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초록

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

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참고문헌

  1. 6G Flagship, "Key drivers and research challenges for 6G ubiquitous wireless intelligence," 6G Research Vision 1, September 2019.
  2. T. J. O'Shea and J. Hoydis, "An introduction to deep learning for the physical layer," IEEE Trans. Cognitive Commun. Netw. vol. 3, 2017, pp. 563-575. https://doi.org/10.1109/TCCN.2017.2758370
  3. J. Lee, "Moving toward 6G," 6G Wireless Summit, Samsung, March 26, 2019.
  4. Samsung Research, "Artificial intelligence," https://research.samsung.com/artificial-intelligence.
  5. Samsung Research, "6G Vision: The next hyper- connected experience for all," https://research.samsung.com/#, 2020.
  6. M. Frodigh, "Towards a connected intelligent future," 6G Wireless Summit, Ericsson, March 26, 2019.
  7. H. Sanneck, "Beyond 5G and artificial intelligence panel," EUCNC 2019, June 21, 2019.
  8. M. Debbah, "Beyond 5G: What will it be?" 6G Wireless Summit, March 24, 2019.
  9. P. Zhu, "B5G + AI: Innovation for next decade," EUCNC 2019, June 21, 2019.
  10. J. C. Belfiore et al., "Towards an intelligent 6G," 6G Wireless Summit, March 26, 2019.
  11. https://www.darpa.mil/attachments/AIFull.pdf
  12. S. Stanczak, "Beyond 5G and artificial intelligence panel-Machine learning for communication," EUCNC 2019, June 21, 2019.
  13. M. Bennis, "Wireless network intelligence," 6G Wireless summit, Univ. of Oulu, March 26, 2019.
  14. A. Alkhateeb et al., "Deep learning coordinated beamforming for highly-mobile millimeter wave systems," IEEE Access, vol. 6, 2018, pp. 37328-37348. https://doi.org/10.1109/ACCESS.2018.2850226
  15. A. M. Elbir, "CNN-based precoder and combiner design in mmWave MIMO systems," IEEE Commun. Lett., vol. 23, no. 7, July 2019, pp. 1240-1243. https://doi.org/10.1109/LCOMM.2019.2915977
  16. T. Lin and Y. Zhu, "Beamforming design for large-scale antenna arrays using deep learning," arXiv: 1904.03657, 2019. https://doi.org/10.1109/lwc.2019.2960581
  17. M. A. Wijaya, K. Fukawa, and H. Suzuki, "Intercell-interference cancellation and neural network transmit power optimization for MIMO channels," in Proc. VTC2015-Fall, Boston, MA, 2015.
  18. H. Sun et al., "Learning to optimize: Training deep neural networks for interference management," IEEE Trans. Signal Process., vol. 66, 2018, pp. 5438-5453. https://doi.org/10.1109/TSP.2018.2866382
  19. E. Nachmani, Y. Be'ery, and D. Burshtein, "Learning to decode linear codes using deep learning," in Proc. UIUC, Monticello, IL, 2016, pp. 341-346.
  20. T. Gruber et al., "On deep learning-based channel decoding," in Proc. CISS, Baltimore, MD, 2017.
  21. W. Lyu et al., "Performance evaluation of channel decoding with deep neural networks," in Proc. ICC, 2018.
  22. S. Cammerer et al., "Scaling deep learning-based decoding of polar codes via partitioning," in Proc. GLOBECOM, 2017.
  23. M. Zhao et al., "Decoding binary linear codes using penalty dual decomposition method," Commun. Letters, vol. 23, no. 6, June 2019, pp. 958-962. https://doi.org/10.1109/LCOMM.2019.2911277
  24. A. Askri and G. Othman, "DNN assisted sphere decoder," in Proc. ISIT, Paris, 2019.
  25. M. Kim et al., "Deep learning-aided SCMA," IEEE Commun. Lett ., vol. 22, no. 4, Apr. 2018.
  26. J.-M. Kang et al., "Deep learning-based MIMO-NOMA with imperfect SIC decoding," IEEE Syst. J., 2019, pp. 1-4.
  27. Y.S. Jeon, S.N. Hong, and N. Lee, "Blind detection for MIMO systems with low-resolution ADCs using supervised learning," in Proc. ICC, 2017.
  28. Z. Jia, W. Cheng, and H. Zhang, "A partial learning based detection scheme for massive MIMO," Wireless Commun. Lett., vol. 8, 2019, pp. 1137-1140. https://doi.org/10.1109/LWC.2019.2909019
  29. Y. Liao et al., "CSI feedback based on deep learning for massive MIMO systems," IEEE Access, vol. 7, 2019, pp. 86810-86820. https://doi.org/10.1109/ACCESS.2019.2924673
  30. C.-K. Wen, W.-T. Shih, and S. Jin, "Deep learning for massive MIMO CSI feedback," Wireless Commun. Lett., vol. 7, no. 5, 2018, pp. 748-751. https://doi.org/10.1109/LWC.2018.2818160
  31. H. Huang et al., "Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system," IEEE Trans. Veh. Technol., vol. 67, no. 9, Sept., 2018, pp. 8549-8560. https://doi.org/10.1109/TVT.2018.2851783
  32. H. He et al., "Deep learning-based channel estimation for beamspace mmWave massive MIMO systems," Wireless Commun. Lett., vol. 7, no. 5, Oct., 2018, pp. 852-855. https://doi.org/10.1109/LWC.2018.2832128
  33. S. Han, Y. Oh, and C. Song, "A deep learning based channel estimation scheme for IEEE 802.11p systems," 2019.
  34. J. Wang et al., "UL-CSI data driven deep learning for predicting DL-CSI in cellular FDD systems," IEEE Access, vol. 7, 2019, pp. 96105-96112. https://doi.org/10.1109/ACCESS.2019.2929091
  35. H. Wu, Z. Sun, and X. Zhou, "Deep learning-based frame and timing synchronization for end-to-end communications," J. Phys., vol. 1169, 2018.
  36. T.A. Chadov, S.D. Erokhin, and A.I. Tikhonyuk, "Machine learning approach on synchronization for FEC enabled channels," in Proc. SYNCHROINFO, 2018.
  37. M. Stark, F. Aoudia, and J. Hoydis, "Joint learning of geometric and probabilistic constellation shaping," arXiv: 1906.07748, 2019.
  38. P. Yang et al., "Adaptive spatial modulation MIMO based on machine learning," IEEE J. Selected Areas Commun., vol. 37, no. 9, 2019, pp. 2117-2131. https://doi.org/10.1109/JSAC.2019.2929404
  39. X. Wang et al., "DeepFi: Deep learning for indoor fingerprinting using channel state information," in Proc. WCNC, 2015.
  40. X. Wang, L. Gao, and S. Mao, "BiLoc: Bi-modal deep learning for indoor localization with commodity 5GHz WiFi," IEEE Access, vol. 5, 2017, pp. 4209-4220. https://doi.org/10.1109/ACCESS.2017.2688362
  41. J. Vieira et al., "Deep convolutional neural networks for massive MIMO fingerprint-based positioning," in Proc. PIMRC, 2017.
  42. W. Zhang et al., "DeepPositioning: Intelligent fusion of pervasive magnetic field and WiFi fingerprinting for smartphone indoor localization via deep learning," in Proc. ICMLA, 2017.