Browse > Article
http://dx.doi.org/10.3837/tiis.2017.06.013

Deep Recurrent Neural Network for Multiple Time Slot Frequency Spectrum Predictions of Cognitive Radio  

Tang, Zhi-ling (Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, Guilin University of Electronic Technology)
Li, Si-min (Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, Guilin University of Electronic Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.6, 2017 , pp. 3029-3045 More about this Journal
Abstract
The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.
Keywords
cognitive radio; spectrum sensing; recurrent neural network; time series;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J.W. Wang, R. Adriman, "Analysis of opportunistic spectrum access in cognitive radio networks using hidden Markov model with state prediction," EURASIP Journal on Wireless Communications and Networks, vol.2015, no.10, pp.1-8, 2015.
2 K.S. Narendra, K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Transaction on Neural Networks, vol.1, no.1, pp. 4-27, 1990.   DOI
3 D. Svozil, V. Kvasnicka, J. Pospichal, "Introduction to multi-layer feed-forward neural networks," Chemometrics and Intelligent Laboratory Systems, vol.39, no.1, pp.43-63, 1997.   DOI
4 S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, USA, pp.161-175, 1999.
5 M. Wellens, J. Riihijarvi, P.Mahonen, "Empirical time and frequency domain models of spectrum use," Phyical. Communication, vol.2, no.1-2, pp.10-32, 2009.   DOI
6 M. Wellens, P. Mahonen, "Lessons learned from an extensive spectrum occupancy measurement campaign and a stochastic duty cycle model," Mobile Networks and Applications, vol.15, no.3, pp.461-474, 2010.   DOI
7 J. G. De Gooijer, R. J. Hyndman, "25 years of Time Series Forecasting," International Journal of Forecasting, vol.22, no.3, pp.443-473, 2006.   DOI
8 M. Hermans, B. Schrauwen, "Training and analyzing deep recurrent neural networks," in Proc. of Advances in Neural Information Processing Systems 26, Lake Tahoe, pp. 190-198, 5-10 December, 2013.
9 V. Nair, G.E. Hinton, "Rectified Linear Units Improve Restricted Boltzmann Machines," in Proc. of International Conference on Machine Learning, Haifa, Israel, 21-24 June 2010.
10 S. B. Taieb, G. Bontempi, A. F. Atiya, "Sorjamaa, A. A review and comparison of strategies for multi-step ahead time series forecasting based on the {NN5} forecasting competition," Expert Syst. Appl., vol.39, no. 8, pp. 7067-7083, 2012.   DOI
11 Z. Chen, N. Guo, Z. Hu, R. Qiu, "Experiment validation of channel state prediction considering delays in practical cognitive radio," IEEE Transactions on Vehicular Technology, vol.60, no.4, pp. 1314-1325, 2011.   DOI
12 C. Hamzacebi, D. Akay, F. Kutay, "Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting," Expert Syst. Appl., vol.36, no.2, pp.3839-3844, 2009.   DOI
13 A. Sorjamaa, J. Hao, N. Reyhani, Y. Ji, A. Lendasse, "Methodology for long-term prediction of time series," Neurocomputing, vol.70, no.16-18, pp.2861-2869, 2007.   DOI
14 A. Sahai, N. Hoven, R. Tandra, "Some fundamental limits on cognitive radio," in Proc. of the Allerton Conference on Communication, Control, and Computing, Monticello, UT, USA, 29 Sep.-1 October, 2004.
15 M. Ghozzi, F. Marx, M. Dohler, J. Palicot, "Cyclostatilonarilty-based test for detection of vacant frequency bands," in Proc. of the 2nd International Conference on Cognitive Radio Oriented Wireless Network and Communication, Mykonos Island, Greek, 8-10 June, 2006.
16 P.D. Sutton, K.E. Nolan, L.E. Doyle, "Cyclostationary signature in practical cognitive radio applications," IEEE JSAC, vol.26, no.1, pp.13-24, 2008.
17 L. Melian-Gutierrez, S. Zazo, J.L. Blanco-Murillo, "Efficiency improvement of HF communications using cognitive radio principles," in Proc. of Ionospheric Radio Systems and Techniques Conference, York, UK, 15-17 May 2012.
18 B. Nicola, R.T. Bheemarjuna, B.S. Manoj, "A Neural Network based Cognitive Controller for Dynamic Channel Selection," in Proc. of IEEE International Conference on Communications, Dresden, Germany, 14-18 June, 2009
19 Z. Chen, N. Guo, Z. Hu, R. Qiu, "Channel state prediction in cognitive radio, part ii: Single-user prediction," in Proc. of IEEE Southeastcon, Nashville, USA 17-20 March, 2011.
20 S. H. Shon, S. J. Jang, Kim J. M, "HMM-based adaptive frequency hopping cognitive radio system to reduce interference time and to improve throughput," KSII transaction on internet. and information system, pp.475-490, 2010.
21 G.C. Tiao, R. S. Tsay, "Some advances in nonlinear and adaptive modeling in Time Series," Journal of Forecasting, vol.13, no.2, pp. 109-131, 1994.   DOI
22 V. Tumuluru, P. Wang, D. Niyato, "Channel status prediction for cognitive radio networks," Wireless Communications & Mobile Computing, vol.12, no.10, pp. 862-874, 2012.   DOI
23 J. Mitola III, "Cognitive radio for flexible mobile multimedia communications," Journal Mobile Networks and Applications, vol.6, no.5, pp. 435-441, 2001.   DOI
24 S. Haykin, "Cognitive radio: Brain-empowered wireless communications," IEEE JSAC, vol.23, no.2, pp.201-220, 2005.
25 S. Haykin, Cognitive Dynamic Systems: Perception-Action Cycle, Radar and Radio, Cambridge University Press, London, pp.14-15, 2012.
26 P. Huang, C.J. Liu, L. Xiao, J. Chen, "Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining," IEEE Transactions on Parallel & Distributed System, vol.25, no.7, pp.1925-1934, 2014.   DOI
27 F.R. Huang,W. Wang, H.Y. Luo, G.D. Yu, Z.Y. Zhang, "Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks," in Proc. of IEEE 71st Vehicular Technology Conference, Taipei, Taiwan, 16-19 May, 2010.
28 V. Tumuluru, P. Wang, D. Niyato, "A neural network based spectrum prediction scheme for cognitive radio," in Proc. of IEEE International Conference on Communications, Cape Town, South Africa, 23-27 May, 2010.