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Recursive Estimation of Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise
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 Title & Authors
Recursive Estimation of Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise
Kim, Namyong;
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The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.
recursive probability;biased zero-error;biased Gaussian;impulsive;underwater communication;
 Cited by
J. Proakis, Digital Communications, McGraw-Hill, 2nd ed, 1989.

Y. Yao and H. Poor, "Blind detection of synchronous CDMA in non-Gaussian channels," IEEE Trans. Signal Processing, vol. 52, no. 1, pp. 271-279, Jan. 2004. crossref(new window)

J. Park, C. Yu, Y. Lee and S. Yoon, "Blind estimation schemes for frequency offset of OFDM systems in non-Gaussian noise environments", Proc. of ICWMC 2012, pp. 13-16, 2012.

J. Park, G. Shevlyakov and K. Kim, "Distributed detection and fusion of weak signals in fading channels with non-Gaussian noises," IEEE Communications Letters, vol. 16, no. 2, pp. 220-223, 2012. crossref(new window)

S. Kim, S. Kim, C. Youn, and Y. Lim, "Performance analysis of receiver for underwater acoustic communications using acquisition data in shallow water", Journal of Acoustical Society of Korea, vol. 29, no. 5, pp. 303-313, May 2010.

A Das, A Kumar and R Bahl, "Realistic ambient noise analysis for passive surveillance algorithm design," Proc. of 2011 IEEE Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies, pp.1-8, 2011.

L. Wang and X. Liu, "Research on underwater target signal detection and recognition processing algorithm," International journal of smart sensing and intelligent systems, vol. 7, no. 4, pp. 1753-1772, Dec. 2014. crossref(new window)

H. Sedarat, K. Fisher, "Multicarrier communication in presence of biased-Gaussian noise sources," Signal Kevin Fisher Remove suggestion Processing, vol. 88, No. 7, July, 2008, pp. 1627-1635.

Z. Daifeng and Q. Tianshuang, "Underwater sources location in non-Gaussian impulsive noise environments," Digital Signal Processing, vol. 16, pp. 149-163, March, 2006. crossref(new window)

N. Kim, "Biased zero-error probability for adaptive systems under non-Gaussian noise," Journal of Internet Computing and Services, vol. 14, no. 1, pp. 9-14, Feb. 2013.

E. Parzen, "On the estimation of a probability density function and the mode," Ann. Math. Stat. vol. 33, p. 1065, 1962. crossref(new window)

N. Kim, "Decision feedback equalizer based on maximization of zero-error probability," Journal of KICS, vol. 36, no. 8, pp. 516-521, Aug. 2011. crossref(new window)

Y. Zheng, J. Jestes, J. Phillips, and F. Li, "Quality and efficiency for kernel density estimates in large data," Proc. of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 433-444, 2013.