2012
DOI: 10.1109/jlt.2012.2210194
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Maximum-Likelihood-Based Blind Dispersion Estimation for Coherent Optical Communication

Abstract: Starting from the maximum likelihood criterion, we derive a novel blind chromatic dispersion (CD) estimation method in the presence of unknown data, propagation delay, polarization state, and differential group delay. By using CD estimation, electronic dispersion compensation (EDC) can be carried out without prior knowledge about the amount of accumulated CD. This adds flexibility to the EDC, which may prove valuable in reconfigurable optical networks. Using numerical simulations, we compare the suggested algo… Show more

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Cited by 13 publications
(4 citation statements)
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“…The static equalizer is used to compensate chromatic dispersion and in this work it is assumed that the link length is known. However, multiple methods to estimate the amount of dispersion are available [27,28]. In this work we use the static equalizer prior to the frame synchronization.…”
Section: Static Equalizationmentioning
confidence: 99%
“…The static equalizer is used to compensate chromatic dispersion and in this work it is assumed that the link length is known. However, multiple methods to estimate the amount of dispersion are available [27,28]. In this work we use the static equalizer prior to the frame synchronization.…”
Section: Static Equalizationmentioning
confidence: 99%
“…In this paper, we assume that the CD is perfectly known at the receiver. In practice, it can be acquired beforehand by using one of the existing CD estimation methods, such as [16].…”
Section: Chromatic Dispersion Compensation With the Frequency Samentioning
confidence: 99%
“…Among all the progress in OPM, the accuracy of CD monitoring directly affects the accuracy of nonlinearity monitoring and it's essential to estimate CD first [6]. Thanks to the rapid development of digital signal processing and powerful algorithms, various CD monitoring methods have been proposed and demonstrated, encompassing those based on radio fre-quency pilot tone or clock tone [7], [8], maximum likelihood criterion [9], [10], separability of histogram [11], delay tap sampling (DTS) [12], auto-correlation of signal power waveform [13], nonlinear effects [14], [15] and machine learning (ML) [16], [17]. Although a majority of the recent OPM researches focus on ML [5], especially the neural network [18], [19], it is extremely easy to get severely overfitting results because the neural network may learn the characteristics of PRBS itself rather than the channels' characteristics [20]- [22].…”
Section: Introductionmentioning
confidence: 99%