Recursive Algorithms for Identification of Impulsive Noise Channels

Serena M. Zabin; H. Vincent Poor

doi:10.1109/18.54878

Recursive Algorithms for Identification of Impulsive Noise Channels

Serena M. Zabin, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

The Class A Middleton model is a widely accepted statistical physical parameteric model for impulsive interference superimposed on a Gaussian background. In this study, a recursive decision-directed estimator for on-line identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated here, and an appropriately modified version is found to yield a recursive algorithm with excellent global performance.

Original language	English (US)
Pages (from-to)	559-578
Number of pages	20
Journal	IEEE Transactions on Information Theory
Volume	36
Issue number	3
DOIs	https://doi.org/10.1109/18.54878
State	Published - May 1990

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

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10.1109/18.54878

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@article{17e7da87ea924502ab1a525b043c1503,

title = "Recursive Algorithms for Identification of Impulsive Noise Channels",

abstract = "The Class A Middleton model is a widely accepted statistical physical parameteric model for impulsive interference superimposed on a Gaussian background. In this study, a recursive decision-directed estimator for on-line identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated here, and an appropriately modified version is found to yield a recursive algorithm with excellent global performance.",

author = "Zabin, {Serena M.} and Poor, {H. Vincent}",

note = "Funding Information: Manuscript received January 21, 1989; revised March 23, 1989. This paper was presented at the 1988 IEEE International Symposium on Information Theory, Kobe, Japan, June 19-24, 1988. This research was supported by the Joint Services Electronics Program (US.A rmy, US. Navy, and U.S. Air Force) under Contract N00014-90-5-1270, The authors are with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801. IEEE Log Nurnher 8933985. 'Class A noise produces negligible transients in a typical receiver and is {"}coherent{"} in this respect. However, it is {"}impulsive{"} in the sense conveyed by the first sentence of the paragraph. Thus, the term {"}impulsive interference{"} is not limited to very short-duration waveforms, hut includes cw trains, structured pulses, etc., as well as {"}impulses.{"} This terminology is consistent with that given, e.g., in [3] and [9]. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.",

year = "1990",

month = may,

doi = "10.1109/18.54878",

language = "English (US)",

volume = "36",

pages = "559--578",

journal = "IEEE Transactions on Information Theory",

issn = "0018-9448",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Zabin, Serena M.

AU - Poor, H. Vincent

N1 - Funding Information: Manuscript received January 21, 1989; revised March 23, 1989. This paper was presented at the 1988 IEEE International Symposium on Information Theory, Kobe, Japan, June 19-24, 1988. This research was supported by the Joint Services Electronics Program (US.A rmy, US. Navy, and U.S. Air Force) under Contract N00014-90-5-1270, The authors are with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801. IEEE Log Nurnher 8933985. 'Class A noise produces negligible transients in a typical receiver and is "coherent" in this respect. However, it is "impulsive" in the sense conveyed by the first sentence of the paragraph. Thus, the term "impulsive interference" is not limited to very short-duration waveforms, hut includes cw trains, structured pulses, etc., as well as "impulses." This terminology is consistent with that given, e.g., in [3] and [9]. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.

PY - 1990/5

Y1 - 1990/5

N2 - The Class A Middleton model is a widely accepted statistical physical parameteric model for impulsive interference superimposed on a Gaussian background. In this study, a recursive decision-directed estimator for on-line identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated here, and an appropriately modified version is found to yield a recursive algorithm with excellent global performance.

AB - The Class A Middleton model is a widely accepted statistical physical parameteric model for impulsive interference superimposed on a Gaussian background. In this study, a recursive decision-directed estimator for on-line identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated here, and an appropriately modified version is found to yield a recursive algorithm with excellent global performance.

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Recursive Algorithms for Identification of Impulsive Noise Channels

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