Nonlinear Interference Mitigation via Deep Neural Networks

A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32×100 km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations.

[1]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[2]  Yann LeCun,et al.  Learning Fast Approximations of Sparse Coding , 2010, ICML.

[3]  Arthur James Lowery,et al.  Improved single channel backpropagation for intra-channel fiber nonlinearity compensation in long-haul optical communication systems. , 2010, Optics express.

[4]  Takeshi Hoshida,et al.  Implementation efficient nonlinear equalizer based on correlated digital backpropagation , 2011, 2011 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference.

[5]  B. Eggleton,et al.  Fiber nonlinearity-induced penalty reduction in CO-OFDM by ANN-based nonlinear equalization. , 2015, Optics letters.

[6]  Alan Pak Tao Lau,et al.  Fiber nonlinearity compensation using extreme learning machine for DSP-based coherent communication systems , 2011, 16th Opto-Electronics and Communications Conference.

[7]  Zabih Ghassemlooy,et al.  Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM , 2015, IEEE Photonics Technology Letters.

[8]  Zhenning Tao,et al.  Multiplier-Free Intrachannel Nonlinearity Compensating Algorithm Operating at Symbol Rate , 2011, Journal of Lightwave Technology.

[9]  J. Kahn,et al.  Compensation of Dispersion and Nonlinear Impairments Using Digital Backpropagation , 2008, Journal of Lightwave Technology.

[10]  Mikael Mazur,et al.  Time-domain digital back propagation: Algorithm and finite-precision implementation aspects , 2017, 2017 Optical Fiber Communications Conference and Exhibition (OFC).

[11]  Yair Be'ery,et al.  Learning to decode linear codes using deep learning , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[12]  Max Tegmark,et al.  Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.