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Application of elliptic discrete Fourier transform type (I) in denoising and receiver design

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Abstract

In this paper, two various applications of elliptic discrete Fourier transform type I (EDFT_I) are presented in the communication area. In the first application, EDFT_I is applied to reduce the additive uniform and Gaussian noise in the sinusoidal signal. The noise reduction is independent from the type of noise and the corresponding amplitude. In the second application, an EDFT_I-based receiver has been proposed which improves the signal to noise at least about 2 dB for the same error-probability as compared with the optimum receiver considering an additive non-Gaussian noise. In this approach, a binary orthogonal signaling is created using the EDFT_I, which cosine and sine signals are used as carrier for 0 and 1 information. Moreover, for an additive non-Gaussian noise, the proper choice of this transform’s parameters as well as the decision threshold, results in improving the accuracy of digital information’s transmission.

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References

  1. Luschi, C., & Mulgrew, B. (2003). Nonparametric trellis equalization in the presence of non-Gaussian interference. IEEE Transactions on Communications, 51, 229–239.

    Article  Google Scholar 

  2. Blum, R. S., Kozick, R. J., & Sadler, B. M. (1999). An adaptive spatial diversity receiver for non-Gaussian interference and noise. IEEE Transactions on Signal Processing, 47(8), 2100–2111.

    Article  Google Scholar 

  3. Sengupta, D., & Kay, S. M. (1989). Efficient estimation of parameters for non-Gaussian autoregressive processes. IEEE Acoustics, Speech, Signal Processing, 37(6), 785–794.

    Article  Google Scholar 

  4. Kuruoglu, E., Molina, C., & Fitzgerald, W. (1998) Approximation of alpha-stable probability densities using finite Gaussian mixtures. In Proceedings of EUSIPCO 98, signal processing IX: Theories and applications (Vol. 2, pp. 989–992).

  5. Grigoryan, A. M. (2011). Two classes of elliptic discrete Fourier transform: Properties and examples. Journal of Mathematical Image and Vision, 39(3), 210–229.

    Article  MATH  Google Scholar 

  6. Grigoryan, A. M. & Grigoryan, M. M. (2009). Discrete integer Fourier transform in real space: Elliptic Fourier transform. In SPIE conference: Electronic imaging, San Diego, CA.

  7. Grigoryan, A. M. (2015). Fourier transforms with rotations on circles or ellipses in signal and image processing. In Proceedings of the SPIE 9411, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015, p. 94110Q.

  8. Grigoryan, A. M., & Grigoryan, M. M. (2009). Brief notes in advanced DSP: Fourier analysis with MATLAB. London/Boca Raton: Taylor & Francis/CRC Press.

    Book  Google Scholar 

  9. Poor, H. V. (1994). An introduction to signal detection and estimation. New York: Springer.

    Book  MATH  Google Scholar 

  10. Proakis, J. G. (2007). Digital communications (5th ed.). New York: McGraw Hill.

    Google Scholar 

  11. Nasir, A., Nazempour, A., & Schober, R. (2009). Adaptive Lp-norm diversity combining in Non-Gaussian noise and interference. IEEE Transactions on Wireless Communications, 8(8), 4230–4240.

    Article  Google Scholar 

  12. Aysal, T., & Barner, K. (2007). Meridian filtering for robust signal processing. IEEE Transactions on Signal Processing, 55, 3949–3962.

    Article  MathSciNet  Google Scholar 

  13. Keller, C., & Pursley, M. (1987). Clipped diversity combining for channels with partial-band interference part I: Clipped-linear combining. IEEE Transactions on Communications, 35, 1320–1328.

    Article  Google Scholar 

  14. Huber, P. (1981). Robust statistics. New York: Wiley.

    Book  MATH  Google Scholar 

  15. Fang, Q., Wang, Y., & Wang, Sh. (2008). The Rao detection of weak signal in Gaussian mixture noise. In Congress on image and signal processing (pp. 542–546).

  16. Bayram, S., & Gezici, S. (2010). On the performance of single-threshold detectors for binary communications in the presence of Gaussian mixture noise. IEEE Transactions on Communications, 58(11), 3047–3053.

    Article  MathSciNet  Google Scholar 

  17. Bhatia, V., & Mulgrew, B. (2007). Non-parametric likelihood based channel estimator for Gaussian mixture noise. Signal Process., 87, 2569–2586.

    Article  MATH  Google Scholar 

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Correspondence to Payman Moallem.

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Moallem, P., Miramirkhani, F. & Sabahi, M. Application of elliptic discrete Fourier transform type (I) in denoising and receiver design. Analog Integr Circ Sig Process 85, 505–512 (2015). https://doi.org/10.1007/s10470-015-0635-7

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  • DOI: https://doi.org/10.1007/s10470-015-0635-7

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