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SCIENTIA SINICA Informationis, Volume 47, Issue 2: 221-234(2017) https://doi.org/10.1360/N112016-00058

Parameter estimator based on the log-cumulant and its performance analysis for heavy-tailed-distributed impulsive interference}{Parameter estimator based on the log-cumulant and its performance analysis for heavy-tailed-distributed impulsive interference

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  • ReceivedMar 22, 2016
  • AcceptedApr 6, 2016
  • PublishedNov 1, 2016

Abstract

The impulsive interference in many wireless communication networks can be modeled as a symmetric $\alpha$-stable distribution noise. The probability density function of the interference needs to be obtained in advance in signal detection, channel decoding, wireless network outage probability, and bit error rate analysis application scenarios. The method of log-cumulant is used to estimate the characteristic exponent and dispersion of the interference. Furthermore, the probability distribution of the estimated parameters is derived in detail, which can be used for quantitative analysis to evaluate the estimation reliability. In addition, the noise in the receiver of the actual system is bivariate mixture noise including the independent Gaussian noise and S$\alpha$S interference. We suggest that the bivariate noise can be approximated from its univariate counterpart. We prove that this type of approximation is reasonable by means of simulation and numerical calculation. The relationship between the parameters of the mixture noise and the geometric power signal-to-noise ratio are provided in this paper based on this assumption. The estimator based on the log-cumulant and performance analysis is therefore still valid in the bivariate noise environment.


Funded by

国家自然科学基金--浙江两化融合联合基金(U1509219)

国家自然科学基金(61471322)

国家自然科学基金(61402416)


References

[1] You X H, Pan Z W, Gao X Q, et al. The 5G mobile communication: the development trends and its emerging key techniques. Sci Sin Inform, 2014, 44: 551-563 [尤肖虎, 潘志文, 高西奇, 等. 5G 移动通信发展趋势与若干关键技术. 中国科学: 信息科学, 2014, 44: 551-563]. Google Scholar

[2] Zhang Z S, Wang X, Zhang C Y, et al. Massive MIMO technology and challenges. Sci Sin Inform, 2015, 45: 1095-1110 [张中山, 王兴, 张成勇, 等. 大规模MIMO关键技术及应用. 中国科学: 信息科学, 2015, 45: 1095-1110]. Google Scholar

[3] Ilow J, Hatzinakos D. Analytic alpha-stable noise modeling in a Poisson field of interferers or scatters. IEEE Trans Signal Process, 1998, 46: 1601-1611 CrossRef Google Scholar

[4] Hughes B L. Alpha-stable models of multiuser interference. In: Proceedings of IEEE International Symposium on Information Theory, Sorrento, 2000. 383-383. Google Scholar

[5] Gulati K, Evans B L, Andrews J G, et al. Statistics of co-channel interference in a field of Poisson and Poisson-Poisson clustered interferers. IEEE Trans Signal Process, 2010, 58: 6207-6222 CrossRef Google Scholar

[6] Zhou Y F, Li R P, Zhao Z F, et al. On the $\alpha$-stable distribution of base stations in cellular networks. IEEE Commun Lett, 2015, 19: 1750-1753 CrossRef Google Scholar

[7] Li R P, Zhao Y F, Qi C, et al. Understanding the traffic nature of mobile instantaneous messaging in cellular networks: a revisiting to $\alpha$-stable models. IEEE J Mag, 2015, 3: 1416-1422. Google Scholar

[8] Pereyra M, Dobigeon N, Batatia H, et al. Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, 2011. 729-723. Google Scholar

[9] Chen J, Nunez-Yanez J L, Achim A. Bayesian video super-resolution with heavy-tailed prior models. IEEE Trans Circ Syst Video Tech, 2014, 24: 905-914 CrossRef Google Scholar

[10] Niranjayan S, Beaulieu N C. The BER optimal linear rake receiver for signal detection in symmetric alpha-stable noise. IEEE Trans Commun, 2009, 57: 3585-3588 CrossRef Google Scholar

[11] Niranjayan S, Beaulieu N C. BER optimal linear combiner for signal detection in symmetric alpha-stable noise: small values of alpha. IEEE Trans Wirel Commun, 2010, 9: 886-890 CrossRef Google Scholar

[12] Rajan A, Tepedelenlioglu C. Diversity combining over rayleigh fading channels with symmetric alpha-stable noise. IEEE Trans Wirel Commun, 2010, 9: 2968-2976 CrossRef Google Scholar

[13] Sureka G, Kiasaleh K. Sub-optimum receiver architecture for AWGN channel with symmetric alpha-stable interference. IEEE Trans Commun, 2013, 61: 1926-1935 CrossRef Google Scholar

[14] Yang F, Zhang X. BER and SER analyses for M-ary modulation schemes under symmetric alpha-stable noise. In: Proceedings of IEEE Global Communications Conference on Wireless Communication Symposium, Austin, 2014. 3983-3988. Google Scholar

[15] Chen Y F, Chen J M. Novel S$\alpha$S PDF approximations and their applications in wireless signal detection. IEEE Trans Wirel Commun, 2015, 14: 1080-1091 CrossRef Google Scholar

[16] Mohhammadreza H B, Hamidreza A. A new alpha and gamma based mixture approximation for heavy-tailed Rayleigh distribution. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, 2015. 3711-3715. Google Scholar

[17] Nikias C L, Shao M. Signal Processing With Alpha-Stable Distributions and Applications. Hoboken: John Wiley & Sons, 1995. 13-14. Google Scholar

[18] Kuruoglu E E, Zerubia J. Modeling SAR images with a generalization of the Rayleigh distribution. IEEE Trans Image Process, 2004, 13: 527-533 CrossRef Google Scholar

[19] Sun Z G, Hang C Z. Estimate of distribution for SAR image based on the generalized Rayleigh distribution. In: Proceedings of the World Congress on Intelligent Control and Automation, Dalian, 2006. 9547-9551. Google Scholar

[20] Sun Z G, Han C Z. Heavy-tailed Rayleigh distribution: a new tool for the modeling of SAR amplitude images. In: Proceedings of International Geoscience and Remote Sensing Symposium, Boston, 2006. 1253-1256. Google Scholar

[21] Pastor G, Mora-Jimenez I, Caamano A J, et al. Log-cumulant matching approximation of heavy-tailed-distributed aggregate interference. In: Proceedings of IEEE Internation Conference Communications, London, 2015. 4811-4815. Google Scholar

[22] Liu T, Cui H G, Mao T, et al. Modeling multilook polarimetric SAR images with heavy-tailed Rayleigh distribution and novel estimation based on matrix log-cumulants. Sci China Inf Sci, 2013, 56: 062306. Google Scholar

[23] Kuruo\u{g}lu E E. Density parameter estimation of skewed $\alpha$-stable distributions. IEEE Trans Signal Process, 2001, 49: 2192-2201 CrossRef Google Scholar

[24] Veillette M. Matlab code alpha-stable distributions. http://math.bu.edu/people/mveillet/html/alphastablepub.html. Google Scholar

[25] Gonzalez J G, Paredes J L, Arce G R. Zero-order statistics: a mathematical framework for the processing and characterization of very impulsive signals. IEEE Trans Signal Process, 2006, 50: 3839-3851. Google Scholar

[26] Nassar M, Gulati K, Sujeeth A, et al. Mitigating near-field interference in laptop embedded wireless transceivers. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Nevada, 2008. 1405-1408. Google Scholar

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