Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Recurrent Neural Network Based Spectrum Sensing Technique for Cognitive Radio Communications

인지 무선 통신을 위한 순환 신경망 기반 스펙트럼 센싱 기법

  • Jung, Tae-Yun (Department of Mobile Convergence and Engineering, Hanbat National University) ;
  • Jeong, Eui-Rim (Department of Information and Communication Engineering, Hanbat National University)
  • Received : 2020.03.05
  • Accepted : 2020.04.06
  • Published : 2020.06.30
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Abstract

This paper proposes a new Recurrent neural network (RNN) based spectrum sensing technique for cognitive radio communications. The proposed technique determines the existence of primary user's signal without any prior information of the primary users. The method performs high-speed sampling by considering the whole sensing bandwidth and then converts the signal into frequency spectrum via fast Fourier transform (FFT). This spectrum signal is cut in sensing channel bandwidth and entered into the RNN to determine the channel vacancy. The performance of the proposed technique is verified through computer simulations. According to the results, the proposed one is superior to more than 2 [dB] than the existing threshold-based technique and has similar performance to that of the existing Convolutional neural network (CNN) based method. In addition, experiments are carried out in indoor environments and the results show that the proposed technique performs more than 4 [dB] better than both the conventional threshold-based and the CNN based methods.

본 논문에서는 인지 무선 통신을 위한 새로운 순환 신경망 기반 스펙트럼 센싱 기법을 제안한다. 제안하는 기법은 주사용자에 대한 정보가 전혀 없는 상황에서 에너지 검출을 통해 신호 존재 유무를 판단한다. 제안 기법은 센싱하고자 하는 전체 대역을 고려하여 수신신호를 고속으로 샘플링 후 이 신호의 FFT (fast Fourier transform)를 통해 주파수 스펙트럼으로 변환한다. 이 스펙트럼 신호는 채널 대역폭 단위로 자른 후 순환 신경망에 입력하여 해당 채널이 사용중인지 비어있는지 판정한다. 제안하는 기법의 성능은 컴퓨터 모의실험을 통해 확인하는데 그 결과에 따르면 기존 문턱값 기반 기법보다 2 [dB] 이상 우수하며 합성곱 신경망 기법과 유사한 성능을 보인다. 또한, 실제 실내환경에서 실험도 수행하는데 이 결과에 따르면 제안하는 기법이 기존 문턱값 기반 방식 및 합성곱 신경망 방식보다 4 [dB] 이상 우수한 성능을 보인다.

Keywords

References

  1. J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios more personal," IEEE personal communications, vol. 6, no. 4, pp. 13-18, Aug. 1999. https://doi.org/10.1109/98.788210
  2. S. Kapoor, S. Rao, and C. Singh, "Opportunistic spectrum sensing by employing matched filter in cognitive radio network," in IEEE Proceeding of International Conference on Communication Systems and Network Technologies, Katra, Jammu, India, pp. 580-583, Jun. 2011.
  3. U. Salama, P. L. Sarker, and A. Chakrabarty, "Enhanced energy detection using matched filter for spectrum sensing in cognitive radio networks," in IEEE Proceeding of the 7th International Conference on Informatics, Electronics & Vision and 2nd International Conference on Imaging, Vision & Pattern Recognition, Kitakyushu, Japan, pp. 185-190, Feb. 2018.
  4. X. Liu, F. Li, and Z. Na, "Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio," IEEE Access, vol. 5, pp. 3801-3812, Mar. 2017. https://doi.org/10.1109/ACCESS.2017.2677976
  5. M. Lopez-Benitez and F. Casadevall, "Improved energy detection spectrum sensing for cognitive radio," IET Communications, vol. 6, no. 8, pp. 785-796, May 2012. https://doi.org/10.1049/iet-com.2010.0571
  6. B. Gajera, D. K. Patel, B. Soni, and M, Lopez-Benitez, "Performance evaluation of improved energy detection under signal and noise uncertainties in cognitive radio networks," in IEEE Proceeding of International Conference on Signals and Systems, Bandung, Indonesia, pp. 131-137, July 2019.
  7. R. R. Jaglan, S. Sarowa, R. Mustafa, S. Agrawal, and N. Kumar, "Comparative study of single-user spectrum sensing techniques in cognitive radio networks," Procedia Computer Science, vol. 58, no. 1, pp. 121-128, Aug. 2015. https://doi.org/10.1016/j.procs.2015.08.039
  8. Y.-J. Tang, Q.-Y. Zhang, and W. Lin, "Artificial neural network based spectrum sensing method for cognitive radio," in IEEE Proceeding of the 6th International Conference on Wireless Communications Networking and Mobile Computing, Chengdu, China, pp. 1-4, Sep. 2010.
  9. M. R. Vyas, D. K. Patel, and M. Lopez-Benitez, "Artificial neural network based hybrid spectrum sensing scheme for cognitive radio," in IEEE Proceeding of the 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Montreal, Canada, pp. 1-7, Oct. 2017.
  10. N. Balwani, D. K. Patel, B. Soni, and M. Lopez-Benitez, "Long short-term memory based spectrum sensing scheme for cognitive radio," in IEEE Proceeding of the 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Istanbul, Turkey, pp. 1-6, Sep. 2019.
  11. K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey," IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp. 2222-2232, Oct. 2017. https://doi.org/10.1109/TNNLS.2016.2582924
  12. T.-Y. Jung, E.-S. Lee, D.-K. Kim, J.-M. Oh, W.-Y. Noh, and E.-R. Jeong, "CNN based spectrum sensing technique for cognitive radio communications," Journal of the Korea Institute of Information and Communication Engineering, vol. 24, no. 2, pp. 276-284, Feb. 2020.