한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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인지 무선 시스템에서 강화학습 기반 협력 센싱 기법

Reinforce Learning Based Cooperative Sensing for Cognitive Radio Networks

  • 김도윤 (아주대학교 컴퓨터공학과) ;
  • 최영준 (아주대학교 컴퓨터공학과) ;
  • ;
  • 최증원 (국방과학연구소)
  • 투고 : 2018.08.18
  • 심사 : 2018.10.15
  • 발행 : 2018.10.31
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초록

본 논문은 인지 무선(CR, Cognitive Radio) 네트워크에서 우선 사용자(Primary User)의 존재 유무를 2차 사용자(Secondary User)가 결정하기 위하여 협력 센싱을 사용하는 환경에서 스펙트럼 센싱의 감지 성능을 높이기 위해 강화 학습(Reinforce learning) 기반으로 최적의 인지 무선 사용자 선택하는 협력 센싱 방안을 제안한다. 협력 센싱을 통해 파악한 전역 센싱 결과와 인지 무선 사용자의 센싱 결과 간의 유사도에 따라 정확도가 높은 사용자를 파악한다. 이 정확도를 강화학습의 보상으로 사용하여 협력 센싱을 수행할수록 전역 결정과 일치하는 센싱 정보를 전송하는 사용자를 선택할 수 있다. 실험 결과 제안한 기법이 기존 협력 센싱 대비 향상된 스펙트럼 감지 성능을 보임을 확인할 수 있다.

In this paper, we propose a reinforce learning based on cooperative sensing scheme to select optimal secondary users(SUs) to enhance the detection performance of spectrum sensing in Cognitive radio(CR) networks. The SU with high accuracy is identified based on the similarity between the global sensing result obtained through cooperative sensing and the local sensing result of the SU. A fusion center(FC) uses similarity of SUs as reward value for Q-learning to determine SUs which participate in cooperative sensing with accurate sensing results. The experimental results show that the proposed method improves the detection performance compared to conventional cooperative sensing schemes.

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참고문헌

  1. M. McHenry and D. McCloskeyk, "Spectrum occupancy measurements: Chicago, Illinois Nov. 16-18, 2005," Shared Spectrum Co. report, 2005.
  2. T. Erpek, M. Lofquist, and K. Patton, "Spectrum Occupancy Measurements: Loring Commerce Centre, Limestone, Maine, September 18-20, 2007," Shared Spectrum Co. Report, 2007.
  3. M. Gandetto and C. Regazzoni, "Spectrum sensing: a distributed approach for cognitive terminals," IEEE J. on selected areas in communications, vol. 25, no. 3, Apr. 2007, pp. 546-557. https://doi.org/10.1109/JSAC.2007.070405
  4. G. Ganesan and Y. Li. "Cooperative spectrum sensing in cognitive radio networks," IEEE Trans. wireless communications, vol. 6, no. 6, June 2007, pp. 2214-2222. https://doi.org/10.1109/TWC.2007.05776
  5. E. Peh and Y. C. Liang, "Optimization for Cooperative Sensing in Cognitive Radio Networks," In Proc. Wireless Communications and Networking Conf.(WCNC), Hong Kong, China, Mar. 2007. pp. 27-32.
  6. J. Ma and Y. Li, "Soft combination and detection for cooperative spectrum sensing in cognitive radio networks," IEEE Trans. Wireless Communications, vol. 7, no. 11, 2007, pp. 4502-4507. https://doi.org/10.1109/T-WC.2008.070941
  7. Z. Li, P. Shi, W. Chen, and Y. Yan, "Square Law Combining Double threshold Energy Detection in Nakagami Channel," Int. J. of Digital Content Technology and its Application, vol. 5, no. 12, Dec. 2011, pp. 307-315. https://doi.org/10.4156/jdcta.vol5.issue12.38
  8. M. Kimon and M. Alouini, Digital communication over fading channels. New York: John Wiley & Sons, Inc. 2 ed. Dec. 2004.
  9. Y. Fu, F. Yang, and Z. He, "A Quantization-Based Multibit Data Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks," Sensors, vol. 18, no. 2, 2018, pp. 473. https://doi.org/10.3390/s18020473
  10. B. A. Bastami and E. Saberinia, "A Practical Multibit Data Combining Strategy for Cooperative Spectrum Sensing," IEEE Trans. Vehicular Technology, vol. 62, no. 1, Jan. 2013, pp. 384-389. https://doi.org/10.1109/TVT.2012.2217160
  11. P. Verma and B. Singh. "On the decision fusion for cooperative spectrum sensing in cognitive radio networks," Wireless Networks vol. 23, no. 7, Oct. 2017, pp. 2253-2262. https://doi.org/10.1007/s11276-016-1285-0
  12. D. Teguig, B. Scheers, and V. Le Nir, "Data fusion schemes for cooperative spectrum sensing in cognitive radio networks," In Proc. Military Communications and Information Systems Conf.(MCC), Gdansk, Poland, Oct. 2012, pp. 1-7.
  13. HB Yilmaz, T. Tugcu, and F. Alagoz, "Novel quantization-based spectrum sensing scheme under imperfect reporting channel and false reports," Int. J. of Communication Systems, vol. 27, no. 10, 2014, pp. 1459-1475. https://doi.org/10.1002/dac.2408
  14. N. Do and B. An. "A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks," Sensors, vol. 15, no. 2, 2015, pp. 4388-4407. https://doi.org/10.3390/s150204388
  15. B. Lo and I. Akyildiz. "Reinforcement learning for cooperative sensing gain in cognitive radio ad hoc networks," Wireless Networks, vol. 19, no. 6, 2013, pp. 1237-1250. https://doi.org/10.1007/s11276-012-0530-4
  16. J. Oksanen, J. Lunden, and V. Koivunen. "Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks," Neurocomputing, vol. 80, 2012 pp. 102-110. https://doi.org/10.1016/j.neucom.2011.07.027
  17. I. Mustapha, B. Ali, M. Rasid, A. Sali, and H. Mohamad, "An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks," Sensors, vol. 15 no. 8, 2005. pp. 19783-19818. https://doi.org/10.3390/s150819783
  18. W. Lee, M. Kim, and D. H. Cho, "Deep Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks," arXiv preprint, arXiv:1705.08164, 2017.
  19. N. S. Shankar, C. Cordeiro, and K. Challapali, "Spectrum agile radios: utilization and sensing architectures," In Proc. IEEE Int. Symp. on Nev Frontiers in Dynamic Spectrum Access Networks(DySPAN) 2005, Baltimore, USA, 2005, pp. 160-169.
  20. C. Watkins and P. Dayan, "Q-learning," Machine learning, vol. 8, no. 3-4, 1992, pp. 279-292. https://doi.org/10.1023/A:1022676722315