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 |
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Pages (from-to) | 3393-3403 |
Number of pages | 11 |
Journal | Signal Processing |
Volume | 86 |
Issue number | 11 |
DOIs | |
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