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

Roman Lisnanski, Anthony J. Weiss

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)3393-3403
Number of pages11
JournalSignal Processing
Volume86
Issue number11
DOIs
StatePublished - 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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