论文信息 - Machine learning concepts in coherent optical communication systems

Machine learning concepts in coherent optical communication systems

Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking

Darko Zibar | Christian G. Schaffer

[1] Toshiaki Koike-Akino,et al. High-order statistical equalizer for nonlinearity compensation in dispersion-managed coherent optical communications. , 2012, Optics express.

[2] B. Nebendahl,et al. Performance Monitoring and Measurement Techniques for Coherent Optical Systems , 2013, Journal of Lightwave Technology.

[3] M. Secondini,et al. Maximum Likelihood Sequence Detection for Mitigating Nonlinear Effects , 2014, Journal of Lightwave Technology.

[4] Maurice O'Sullivan,et al. Advances in High-Speed DACs, ADCs, and DSP for Optical Coherent Transceivers , 2014, Journal of Lightwave Technology.

[5] Idelfonso Tafur Monroy,et al. Stokes Space-Based Optical Modulation Format Recognition for Digital Coherent Receivers , 2013, IEEE Photonics Technology Letters.

[6] Chao Lu,et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. , 2012, Optics express.

[7] Ole Winther,et al. Joint Iterative Carrier Synchronization and Signal Detection Employing Expectation Maximization , 2014, Journal of Lightwave Technology.

[8] Idelfonso Tafur Monroy,et al. Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission. , 2012, Optics express.