전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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기계학습을 활용한 5G통신 동향

Research Trends on 5G Communications using Machine Learning

  • 발행 : 2016.10.01
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초록

빅데이터를 통한 학습, GPU를 활용한 고속 컴퓨팅 및 다양한 알고리즘 개발과 더불어 기계학습은 다양한 분야에서 종래에 이루어내지 못한 뛰어난 성과를 달성하고 있다. 그동안 상용화된 통신 시스템에서 기계학습이 활성화되지 못했지만, 전례없는 다양한 서비스와 단말을 아우르는 5G 통신에서는 더욱 적극적으로 활용될 것으로 예상된다. 기계학습은 링크 적응 등 무선접속기술, 다양한 망이 혼재된 이종망 기술, 트래픽 분류 등을 위한 네트워크 기술, 침입 탐지를 위한 보안 기술 등 다양한 통신기술에서 연구됐다. 또한, 최근에는 유럽의 Public Private Partnership(5G PPP) 프로젝트를 비롯하여 다양한 그룹에서 활발히 연구되고 있으며, 컬컴/노키아/에릭슨 등 통신 관련 기업들도 적극적인 투자를 하고 있다. 본고에서는 기계학습 관련 통신기술, 연구그룹 및 기업 동향을 소개하고, 이를 통해 5G 통신 적용 가능성을 짚어본다.

키워드

참고문헌

  1. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, 1995, pp. 273-297.
  2. 한국전자통신연구원, "5G Insight White Paper: 5G Vision & Enabling Technologies," 2015. 12.
  3. R.C. Daniels, C.M. Caramanis, and R.W. Heath, "Adaption in Convolutionally-Coded MIMO-OFDM Wireless Systems through Supervised Learning and SNR Ordering," IEEE Trans. Veh. Technol., vol. 59, no. 1, pp. 114-126, Jan. 2010. https://doi.org/10.1109/TVT.2009.2029693
  4. R.C. Daniels and R.W. Heath, "An Online Learing Framework for Link Adaptation in Wireless Networks," Proc. Inf. Theory Appl. Workshop, Feb. 2009, pp. 138-140.
  5. A. Rico-Alvarino and R.W. Heath, "Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM," Submitted to IEEE Transactions on Wireless Communications, 2013.
  6. S.S. Hong and S.R. Katti, "Dof: A Local Wireless Information Plane," ACM SIGCOMM, vol. 41, no. 4, Aug. 2011, pp. 230-241. https://doi.org/10.1145/2043164.2018463
  7. K. Joshi, S. Hong, and S. Katti, "PinPoint: Localizing Interfering Radios," Proc. USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2013.
  8. X. Wang, X. Li, and V. C.M. Leung, "Artificial Intelligence-Based Techniques for Emerging Heterogeneous Networks: State of the Arts, Opportunities, and Chanllenges," IEEE Access, vol. 3, Aug. 2015, pp. 1379-1391. https://doi.org/10.1109/ACCESS.2015.2467174
  9. M.A. Khan, H. Tembine, and A.V. Vasilakos, "Game Dynamics and Cost of Learning in Heterogeneous 4G Networks," IEEE J. Sel. Areas Commun., vol. 30, no. 1, Jan. 2012, pp. 198-213. https://doi.org/10.1109/JSAC.2012.120118
  10. M. Simsek, M. Bennis, and I. Guvenc, "Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach," in Proc. Netw. Internet Archit., May 2015.
  11. Z. Zhao, J. Chen, and N. Crespi, "A Policy-Based Framework for Autonomic Reconfiguration Management in Heterogeneous Networks," Proc. 7th Int. Conf. Mobile Ubiquitous Multimedia, 2008, pp. 71-78.
  12. P.T. Semov et al., "Use of Positioning Information for Performance Enhancement of Uncoordinated Heterogeneous Network Deployment," Proc. 3rd Int. Conf. VITAE, June 2013, p. 16.
  13. P.T. Semov et al. "Increasing Throughput and Fairness for Users in Heterogeneous Semi Coordinated Deployments," Proc. IEEE WCNC, Apr. 2014, pp. 40-45.
  14. Q. Li et al., "Dynamic Enhanced Inter-Cell Interference Coordination Using Reinforcement Learning Approach in Heterogeneous Network," Proc. IEEE ICCT, Nov. 2013, pp. 239-243.
  15. W. Guo et al., "Spectral- and Energy-Efficient Antenna Tilting in a HetNet Using Reinforcement Learning," Proc. IEEE WCNC, Apr. 2013, pp. 767-772.
  16. M. Simsek, M. Bennis, and A. Czylwik, "Coordinated Beam Selection in LTE-Advanced HetNets: A ReinForcement Learning Approach," Proc. IEEE Globecom Workshops, Dec. 2012, pp. 603-607.
  17. M. Simsek and A. Czylwik, "Improved Decentralized Fuzzy Q-Learning for Interference Reduction in Heterogeneous LTE-Networks," Proc. Int. OFDM Workshop, Aug. 2012, pp. 1-6.
  18. M. Dirani and Z. Altman, "Self-Organizing Networks in Next Generation Radio Access Networks: Application to Fractional Power Control," Computer Networks, vol. 55, no. 2, Feb. 2011, pp. 431-438. https://doi.org/10.1016/j.comnet.2010.08.012
  19. S. Fan, H. Tian, and C. Sengul, "Self-Optimization of Coverage and Capacity Based on a Fuzzy Neural Network with Cooperative Reinforcement Learning," EURASIP J. Wireless Commun. Netw., vol. 2014, no. 1, pp. 57:1-57:14, Apr. 2014.
  20. R. Chai et al., "Neural Network based Vertical Handoff Performance Enhancement in Heterogeneous Wireless Networks," Proc. WiCOM, Sept. 2011, pp. 1-4.
  21. Zander, S., Nguyen, T., & Armitage, G., "Automated Traffic Classification and Application Identification Using Machine Learning," IEEE Conf. Local Computer Networks 30th Anniversary, Nov. 2005, pp. 250-257.
  22. S. Zander and G. Armitage, "Practical Machine Learning based Multimedia Traffic Classification for Distributed QoS Management," Local Computer Networks(LCN) IEEE 36th Conference, Oct. 2011, pp. 399-406.
  23. T.T. Nguyen et al., "Timely and Continuous Machine-Learning-based Classification for Interactive IP Traffic," IEEE/ACM Transactions on Networking (TON), vo. 20, no. 6, 2012, pp. 1880-1894. https://doi.org/10.1109/TNET.2012.2187305
  24. A. McGregor, "Flow Clustering Using Machine Learning Techniques," International Workshop on Passive and Active Network Measurement Springer Berlin Heidelberg, Apr. 2004, pp. 205-214.
  25. T.T. Nguyen and G. Armitage, "A Survey of Techniques for Internet Traffic Classification using Machine Learning," IEEE Communications Surveys & Tutorials, vol. 10, no. 4, 2008. pp. 56-76. https://doi.org/10.1109/SURV.2008.080406
  26. http://arxiv.org/abs/1503.08855
  27. J. Cho et al., "Dynamic Learning Model Update of Hybrid-Classifiers for Intrusion Detection," Journal of Supercomputing, vol. 64, no. 2, 2013. pp. 522-526. https://doi.org/10.1007/s11227-011-0698-x
  28. C.F. Tsai and C.Y. Lin, "A Triangle Area based Nearest Neighbors Approach to Intrusion Detection," J. Pattern Recognition, vol. 43, no. 1, Jan. 2010. pp. 222-229. https://doi.org/10.1016/j.patcog.2009.05.017
  29. K.K. Patel and B.V. Buddhadev, "Machine Learning based Research for Network Intrusion Detection: A State-ofthe-Art," International Journal Information and Network Security (IJINS), vol. 3, no. 3. 2014.
  30. R. Sommer and V. Paxson, "Outside the Closed World: on Using Machine Learning for Network Intrusion Detection," IEEE Symposium on Security and Privacy, May. 2010, pp. 305-316.
  31. CogNet, "Initial Use Cases, Scenarios and Requirement," 2015.11.
  32. https://www.qualcomm.com/news/onq/2015/03/02/qualcommzeroth-advancing-deep-learning-devices-video
  33. https://www.qualcomm.com/news/releases/2016/05/02/qualcomm-helps-make-your-mobile-devices-smarternew-snapdragon-machine
  34. https://blog.networks.nokia.com/mobile-networks/2015/11/11/machine-learning-teach-networks-self-aware/
  35. M. Svensson, and J. Soderberg, "Machine-Learning Technologies in Telecommunications," Ericsson Review, 2008.