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Fuzzy Model of 16PSK and 16QAM Modulation

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In the paper, a concept of Additive Fuzzy Noise (AFN) channel is introduced. The theoretical equations are derived for Bit Error Rate (BER) and Symbol Error Rate (SER) with some digital modulation scheme in the AFN channel. Following modulations are considered: Phase Shift Keying (16PSK), Quadrature Amplitude Modulation (16QAM). The fuzzy approach to these modulations is presented. The BER and SER values are calculated using possibility theory. The results obtained by fuzzy noise model are compared with conventional approach, where probability models of the noise are used.
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Bibliografia
  • [1] J. G. Proakis, Digital Communication, 4th ed. New York: McGrow-Hill, 2001.
  • [2] S. Haykin and M. Moher, Communication Systems. John Wiley & Sons, 2009.
  • [3] M. P. Fitz, “Fundamentals of communications systems,” in Communications Engineering. New York: McGrow-Hill, 2007.
  • [4] B. S. Butkiewicz, “Towards Fuzzy Fourier Transform,” in Eleventh International Conference Information Processing and Management of Uncertainty in Knowledge-based Systems, Paris, France, 2-7 July 2006, pp. 2560-2565.
  • [5] B. S. Butkiewicz, “An Approach to Theory of Fuzzy Signals Basic Definitions,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 982-993, 2008.
  • [6] B. S. Butkiewicz, “Fuzzy approach to correlation function,” in Lecture Notes in Artificial Intelligence, vol. 4029. Berlin, New York: Springer-Verlag, 2006, pp. 202-211.
  • [7] B. S. Butkiewicz, “Fuzzy analog and discrete time invariant systems,” Proceedings of SPIE, vol. 6937, pp. 1-8, 2007, invited paper 693736.
  • [8] B. S. Butkiewicz, “An approach to theory of fuzzy discrete signals,” in Foundation of Fuzzy Logic and Soft Computing, P. Mellin et. all., Ed., vol. 4529, Lecture Notes in Artificial Intelligence. Berlin, New York: Springer-Verlag, 2007, pp. 646-655.
  • [9] B. S. Butkiewicz, “Fuzzy digital filters with triangular norms,” in Artificial Intelligence and Soft Computing, R. Rutkowski et all., Ed., vol. 6113, Lecture Notes in Artificial Intelligence. Berlin, New York: Springer-Verlag, 2010, pp. 19-26.
  • [10] B. S. Butkiewicz, “Fuzzy Model of QPSK and QAM Modulation,” in Emerging Intelligent Technologies in Industry, D. Ryko, P. Gawrysiak, H. Rybinski, and M. Kryszkiewicz, Eds., 2011, pp. 297-305.
  • [11] I. Cousoa and J. Sánchez, “Higher order models for fuzzy random variables,” Fuzzy Sets and Systems, 2011, article in press.
  • [12] L. A. Zadeh, “Fuzzy set as a basis for a theory of possibility,” Fuzzy Sets and Systems, vol. 1, no. 1, pp. 3-28, 1978.
  • [13] T. Sudkamp, “On probability-possibility transformations,” Fuzzy Sets and Systems, vol. 51, pp. 73-81, 1992.
  • [14] J. R. Barry, D. G. Messerschmitt, and E. A. Lee, Digital Communication, 3rd ed. Dordrecht, Netherlands: Kluwer Academic Publishers, 2004.
  • [15] A. D. Luca and S. Termini, “A definition of non probabilistic entropy in the setting of fuzzy set theory,” Information and Control, vol. 20, pp. 301-312, 1972.
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bwmeta1.element.baztech-article-BWA0-0051-0002
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