Browse > Article
http://dx.doi.org/10.6109/jkiice.2020.24.6.759

Recurrent Neural Network Based Spectrum Sensing Technique for Cognitive Radio Communications  

Jung, Tae-Yun (Department of Mobile Convergence and Engineering, Hanbat National University)
Jeong, Eui-Rim (Department of Information and Communication Engineering, Hanbat National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.6, 2020 , pp. 759-767 More about this Journal
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.
Keywords
Cognitive radio; Recurrent neural network; Spectrum sensing; Energy detection; Binary classification;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.