Low complexity generalized EM algorithm for blind channel estimation and data detection in optical communication systems

Roman Lisnanski; Anthony J. Weiss

doi:10.1016/j.sigpro.2006.02.037

Low complexity generalized EM algorithm for blind channel estimation and data detection in optical communication systems

Roman Lisnanski^*, Anthony J. Weiss

^*Corresponding author for this work

Department of Electrical Engineering - Systems

Tel Aviv University

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

6 Scopus citations

Abstract

Expectation-Maximization algorithm (EM) has been used in the past for blind estimation of intersymbol interference channels characterized by additive white Gaussian noise. When the channel is characterized by non-Gaussian, signal-dependent noise, the computational complexity of direct application of EM becomes prohibitively high. In this paper, a low complexity generalized EM algorithm is presented. The proposed algorithm achieves a major reduction in computational complexity compared to the EM algorithm and can be applied to nonlinear finite memory channels with non-Gaussian signal-dependent noise. Simulation results are presented for intensity modulated direct detection optical channel that is characterized by non-central chi-square distribution noise.

Original language	English
Pages (from-to)	3393-3403
Number of pages	11
Journal	Signal Processing
Volume	86
Issue number	11
DOIs	https://doi.org/10.1016/j.sigpro.2006.02.037
State	Published - Nov 2006

Keywords

Baum-Welch algorithm
Blind channel estimation
Generalized EM
Hidden Markov Models
Maximum likelihood
Non-central chi-square noise
Non-gaussian signal-dependent noise

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10.1016/j.sigpro.2006.02.037

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title = "Low complexity generalized EM algorithm for blind channel estimation and data detection in optical communication systems",

abstract = "Expectation-Maximization algorithm (EM) has been used in the past for blind estimation of intersymbol interference channels characterized by additive white Gaussian noise. When the channel is characterized by non-Gaussian, signal-dependent noise, the computational complexity of direct application of EM becomes prohibitively high. In this paper, a low complexity generalized EM algorithm is presented. The proposed algorithm achieves a major reduction in computational complexity compared to the EM algorithm and can be applied to nonlinear finite memory channels with non-Gaussian signal-dependent noise. Simulation results are presented for intensity modulated direct detection optical channel that is characterized by non-central chi-square distribution noise.",

keywords = "Baum-Welch algorithm, Blind channel estimation, Generalized EM, Hidden Markov Models, Maximum likelihood, Non-central chi-square noise, Non-gaussian signal-dependent noise",

author = "Roman Lisnanski and Weiss, {Anthony J.}",

year = "2006",

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doi = "10.1016/j.sigpro.2006.02.037",

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AU - Lisnanski, Roman

AU - Weiss, Anthony J.

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Y1 - 2006/11

N2 - Expectation-Maximization algorithm (EM) has been used in the past for blind estimation of intersymbol interference channels characterized by additive white Gaussian noise. When the channel is characterized by non-Gaussian, signal-dependent noise, the computational complexity of direct application of EM becomes prohibitively high. In this paper, a low complexity generalized EM algorithm is presented. The proposed algorithm achieves a major reduction in computational complexity compared to the EM algorithm and can be applied to nonlinear finite memory channels with non-Gaussian signal-dependent noise. Simulation results are presented for intensity modulated direct detection optical channel that is characterized by non-central chi-square distribution noise.

AB - Expectation-Maximization algorithm (EM) has been used in the past for blind estimation of intersymbol interference channels characterized by additive white Gaussian noise. When the channel is characterized by non-Gaussian, signal-dependent noise, the computational complexity of direct application of EM becomes prohibitively high. In this paper, a low complexity generalized EM algorithm is presented. The proposed algorithm achieves a major reduction in computational complexity compared to the EM algorithm and can be applied to nonlinear finite memory channels with non-Gaussian signal-dependent noise. Simulation results are presented for intensity modulated direct detection optical channel that is characterized by non-central chi-square distribution noise.

KW - Baum-Welch algorithm

KW - Blind channel estimation

KW - Generalized EM

KW - Hidden Markov Models

KW - Maximum likelihood

KW - Non-central chi-square noise

KW - Non-gaussian signal-dependent noise

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SN - 0165-1684

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SP - 3393

EP - 3403

JO - Signal Processing

JF - Signal Processing

IS - 11

ER -

Low complexity generalized EM algorithm for blind channel estimation and data detection in optical communication systems

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Keywords

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