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2015 | 5 | 55--66
Tytuł artykułu

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model. Index Terms-Cognitive Radio; Primary User; Artificial Neural Network; Support Vector Machine; Compact Differential Evolution; RF Power; Prediction.(original abstract)
Słowa kluczowe
Rocznik
Tom
5
Strony
55--66
Opis fizyczny
Twórcy
autor
  • Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, England
autor
  • Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, England
autor
  • Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, England
autor
  • Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, England
  • Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, England
Bibliografia
  • FCC, "Federal comminucation commission notice of inquiry and notice of proposed rule making, in the matter of establishment of an interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed, mobile and satellite frequency bands," no. 03-237, November 13, 2003.
  • V. Valenta, R. Marsalek, G. Baudoin, M. Villegas, M. Suarez, and F. Robert, "Survey on spectrum utilization in europe: Measurements, analyses and observations," in Cognitive Radio Oriented Wireless Networks Communications (CROWNCOM), 2010 Proceedings of the Fifth International Conference on, June 2010, pp. 1-5.
  • S. Haykin, D. J. Thomson, and J. H. Reed, "Spectrum sensing for cognitive radio," in IEEE Transactions on Cognitive Radio, May 2009.
  • J. Oh and W. Choi, "A hybrid cognitive radio system: A combination of underlay and overlay approaches," in IEEE Transactions on Cognitive Radio, 2009.
  • C. Stevenson, G. Chouinard, Z. Lei, W. Hu, J. Stephen, and W. Caldwell, "The first cognitive radio wireless regional area network standard," in IEEE 802.22, 2009.
  • X. Xing, T. Jing, W. Cheng, Y. Huo, and X. Cheng, "Spectrum prediction in cognitive radio networks," in 1536-1284/13/$25.00 c 2013 IEEE Transactions on Wireless Communications, April 2013.
  • A. M. Wyglinski, M. Nekovee, and Y. T. Hou, Cognitive Radio Communications and Networks, 2009.
  • M. Subhedar and G. Birajdar, "Spectrum sensing techniques in cognitive radio networks: A survey," International Journal of Next-Generation Networks, vol. 3, no. 2, pp. 37-51, 2011.
  • T. W. Rondeau, B. Le, C. J. Rieser, and C. W. Bostian, "Cognitive radios with genetic algorithms: Intelligent control of software defined radios," in c 2004 SDR Forum, Proceeding of the SDR 2004 Technical Conference and Product Exposition, 2004.
  • S. K. Udgata, K. P. Kumar, and S. L. Sabat, "Swarm intelligence based resource allocation algorithm for cognitive radio network," in Parallel Distributed and Grid Computing (PDGC), 2010 1st International Conference on, Oct 2010, pp. 324-329.
  • M. Matinmikko, J. Del Ser, T. Rauma, and M. Mustonen, "Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems," Selected Areas in Communications, IEEE Journal on, vol. 31, no. 11, pp. 2173-2184, November 2013.
  • L. Giupponi and A. Perez, "Fuzzy-based spectrum handoff in cognitive radio networks," 2008.
  • Y. Chen and H.-S. Oh, "A survey of measurement-based spectrum occupancy modelling for cognitive radios," in 1553-877X c 2013 IEEE IEEE Communications Surveys and Tutorials, 2013.
  • R. Azmi, "Support vector machine based white space predictors for cognitive radio," Master's thesis, 2011.
  • O. Winston, A. Thomas, and W. OkelloOdongo, "Optimizing neural network for tv idle channel prediction in cognitive radio using particle swarm optimization," in Computational Intelligence, Communication Systems and Networks (CICSyN), 2013 Fifth International Conference on, June 2013, pp. 25-29.
  • M. I. Taj and M. Akil, "Cognitive radio spectrum evolution prediction using artificial neural networks based mutivariate time series modelling," in European Wireless, Vienna Austria, April 2011.
  • X. Li and S. A. Zekavat, "Traffic pattern prediction and performance investigation for cognitive radio systems," in IEEE Communication Society, WCNC Proceedings, 2008.
  • S. Hiremath and S. K. Patra, "Transmission rate prediction for cognitive radio using adaptive neural fuzzy inference system," in IEEE 5th International Conference on Industrial and Information Systems (ICIIS), India, Aug 2010.
  • S. Geirhofer, J. Z. Sun, L. Tong, and B. M. Sadler, "Cognitive frequency hopping based on interference prediction: Theory and experimental results," vol. 13, no. 2, march 17, 2009.
  • Z. Tabakovic, S. Grgic, and M. Grgic, "Fuzzy logic power control in cognitive radio," in IEEE transactions, 2009.
  • Z. Lin, X. Jian, L. Huang, and Y. Yao, "Energy prediction based spectrum sensing approach for cognitive radio network," in 978-1-4244- 3693-4/09/$25.00 c 2009 IEEE, 2009.
  • S. Haykin, Neural Networks and Learning Machines, 3rd ed., 2008.
  • Z. Jianli, "Based on neural network spectrum prediction of cognitive radio," in 978-1-4577-0321-8/11/$26.00 c 2011 IEEE, 2011.
  • V. Vapnik, The nature of statistical learning theory. Springer-Verlag New York Inc, 1999.
  • V. Vapnik, Statistical learning theory. New York: Wiley, 1998.
  • E. Alpaydin, Introduction to Machine Learning, ser. Adaptive computation and machine learning. MIT Press, 2004.
  • V. Kecman, Learning and soft computing. MIT Press Cambridge, Mass, 2001.
  • C. Vladimir and Y. MA, "Selection of meta-parameters for support vector regression," pp. 687-693, August 2002.
  • W. Wenjian, Z. Xu, W. Lu, and X. Zhang, "Determination of the spread parameter in the gaussian kernel for classification and regression," vol. 55, no. 3, pp. 643-663, October 2003.
  • V. S. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory, and Methods, 1st ed. New York, NY, USA: John Wiley and Sons, Inc., 1998.
  • B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2001.
  • K. V. Price, R. Storn, and J. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Springer, 2005.
  • A. K. Qin, V. L. Huang, and P. N. Suganthan, "Differential evolution algorithm with strategy adaptation for global numerical optimization," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, April 2009.
  • D. Zaharie, "A comparative analysis of crossover variants in differential evolution," in Proceedings of the International Multiconference on Computer Science and Information Technology, 2007, pp. 171-181.
  • E. Mininno, F. Neri, F. Cupertino, and D. Naso, "Compact differential evolution," Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 32-54, Feb 2011.
  • G. Harik, F. Lobo, and D. Goldberg, "The compact genetic algorithm," Evolutionary Computation, IEEE Transactions on, vol. 3, no. 4, pp. 287-297, Nov 1999.
  • C. W. Ahn and R. Ramakrishna, "Elitism-based compact genetic algorithms," Evolutionary Computation, IEEE Transactions on, vol. 7, no. 4, pp. 367-385, Aug 2003.
  • A. H. Sung, "Ranking importance of input parameters of neural networks," Expert Systems with Applications, vol. 15, no. 3, pp. 405-411, November 1998.
  • F. Wilcoxon, "Individual comparisons by ranking methods," Biometrics Bulletin, vol. 1, no. 6, pp. 80-83, 1945.
  • S. Iliya, E. Goodyer, J. Shell, J. Gow, and M. Gongora, "Optimized neural network using differential evolutionary and swarm intelligence optimization algorithms for rf power prediction in cognitive radio network: A comparative study," in 978-1-4799-4998-4/14/$31.00 c 2014 IEEE International Conference on Adaptive Science and Information Technology, 2014.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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