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Stochastic resonance for estimation of a signal’s bandwidth under low SNR

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Abstract

Traditional bandwidth estimation has low accuracy under low signal-to-noise ratio (SNR). To solve this problem, a new method of bandwidth estimation based on stochastic resonance is proposed. This method will process the signal twice by means of stochastic resonance, which improves the SNR while reducing fluctuations of the amplitude spectrum edge, and then calculating the local mean value function of the amplitude spectrum. A bandwidth can be estimated by using the starting and ending points where the amplitude value of the local mean function is greater than a certain threshold. The experimental simulations show that this method can accurately estimate the bandwidth of the phase shift key empty, quadrature amplitude modulation, and orthogonal frequency modulation at low SNRs. For example, the estimation accuracy could reach 90 % when the SNR is −10 dB. It is easy to conclude that the proposed method surpasses traditional bandwidth estimation method in terms of accuracy.

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References

  1. Benzi, R., Sutera, A., & Vulpiani, A. (1981). The mechanism of stochastic resonance. Physica A, 14(11), 453–457.

    MathSciNet  Google Scholar 

  2. Benzi, R., Parisi, G., & Vulpiani, A. (1983). Theory of stochastic resonance in climatic change. SIAM Journal on Applied Mathematics, 43(3), 565–578.

    Article  MathSciNet  MATH  Google Scholar 

  3. Yang, Y.B., Xu, B.H. (2011) A review of parameter-induced stochastic resonance and current applications in two-dimensional image processing. Springer Verlag Solid Mechanics and its Applications, 29, 229–238.

  4. Jha, R. K., Biswas, P. K., & Chatterji, B. N. (2010). Contrast enhancement of dark images using stochastic resonane. IET Image Processing, 6(3), 230–237.

    Article  MathSciNet  Google Scholar 

  5. Ashida, G., & Kubo, S. (2010). Suprathreshold stochastic resonance induced by ion channel fluctuation. Physica D Nonlinear Phenomena, 239(6), 327–334.

    Article  MATH  Google Scholar 

  6. Repperger, W. D., & Farris, A. K. (2010). Stochastic resonance—A nonlinear control theory interpretation. International Journal of Systems Science, 41(7), 897–907.

    Article  MathSciNet  MATH  Google Scholar 

  7. Landa, P., Ushakov, V., & Kurths, J. (2006). Rigorous theory of stochastic resonance in overdamped bistable oscillators for weak signals. Chaos, Solitons & Fractals, 30(3), 574–578.

    Article  Google Scholar 

  8. Lu, L. S., He, B. Q., & Kong, R. F. (2014). On-line weak signal detection via adaptive stochastic resonance. Review of Scientific Instruments, 85(6), 066111.

    Article  Google Scholar 

  9. Weiss, G. L. (1994). Wavelets and wideband correlation processing. IEEE Signal Processing Magazine, 11(1), 13–32.

    Article  Google Scholar 

  10. Peebles, Z. P. (1987). Probability, Random Variables, and Random Signal Principles. New York: McGraw-Hill Book Co.

    Google Scholar 

  11. Kesler, B. S., & Haykin, S. (1987). Maximum entropy method applied to the spectral analysis of radar clutter. IEEE Transactions on Information Theory, 24(2), 269–272.

    Article  Google Scholar 

  12. Naraghi, M. P., & Ikuma, T. (2010). Autocorrelation-based spectrum sensing for cognitive radios. IEEE Transactions on Vehicular Technology, 59(2), 718–733.

    Article  Google Scholar 

  13. Ge,X.F., Meng,D.H., Peng,N.Y., & Wang,T.X.(2001). New methods for estimating the center frequency and bandwidth of clutter. Chinese Institute of Electronics (CIE) International Conference on Radar Proceedings, 1059–1061.

  14. Ge, F. X., Meng, H. D., Peng, Y. N., & Wang, X. T. (2002). Clutter central frequency and bandwidth estimation methods. Qinghua Daxue Xuebao/Journal of Tsinghua University, 42(7), 941–944.

    Google Scholar 

  15. Tsiakoulis, P., Potamianos, A., & Dimitriadis, D. (2013). Instantaneous frequency and bandwidth estimation using filterbank arrays. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, 8032–8036.

  16. Guo, F. (2009). Stochastic resonance in a bias monstable system with frequency mixing force and multiplicative and additive noise. Physica A, 12(15), 2315–2320.

    Article  Google Scholar 

  17. MeNamara, B., & Wiesenfeld, K. (1988). Observation of stochastic resonance in a ring laser. Physica Review Letter, 1(4), 3–4.

    Google Scholar 

  18. Wellens, T., & Buchleither, A. (2001). Bistability and stochastic resonance in an open quantum system. Physica Letter A, 12(15), 131–149.

    Google Scholar 

  19. Fox, F. R. (1989). Stochastic resonance in a double well. Physica Review A, 39(8), 4141–4153.

    Article  MathSciNet  Google Scholar 

  20. Collins, J. J., Chow, C. C., Capela, C. A., & Imhoff, T. T. (1995). A period stochastic resonance. Physical Review E, 52(4), 5575–5584.

    Article  Google Scholar 

  21. Heneghan, C., & Chow, C. C. (1996). Information measures quantifying aperiodic stochastic resonance. Physical Review E, 54(4), 2228–2231.

    Article  Google Scholar 

  22. Li, W., Zhang,Y., Zhang, Y., Huang, L.K., Cosmas,J., & Maple,C. (2013).Subspace-based SNR estimator for OFDM system under different channel conditions. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.

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Acknowledgments

The author would like to give an acknowledgment to authors of references and a great deal of work done by them, as well as the co-workers for their helpful comments.

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Authors and Affiliations

  1. Department of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China

    Xiao Wang & Yong Gao

Authors
  1. Xiao Wang
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  2. Yong Gao
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Correspondence to Xiao Wang.

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Wang, X., Gao, Y. Stochastic resonance for estimation of a signal’s bandwidth under low SNR. Analog Integr Circ Sig Process 89, 263–269 (2016). https://doi.org/10.1007/s10470-016-0809-y

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  • DOI: https://doi.org/10.1007/s10470-016-0809-y

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