Co-GRU Enhanced End-to-End Design for Long-haul Coherent Transmission Systems

In recent years, the end-to-end (E2E) scheme based on deep learning (DL) has been proposed as a potential scheme to jointly optimize the encoder and the decoder parameters of the optical communication system. Compared with conventional deep neural network (DNN) adopted in E2E design, center-oriented Gated Recurrent Unit (Co-GRU) network has the ability to learn and compensate for inter-symbol interference (ISI) with low computation cost while satisfying the gradient backpropagation (BP) condition. In this paper, the Co-GRU structure is adopted for both channel modeling and decoder implementation in E2E design for long-haul coherent wavelength division multiplexing (WDM) transmission systems, which can enhance the performance of general mutual information (GMI) and Q2-factor. For the E2E system with Co-GRU based decoder, the gain of GMI and Q2-factor are respectively improved 0.2 bits/sym and 0.48dB, compared to that of the conventional QAM system, for a 5-channel dual-polarization coherent system transmitting over 960km standard single mode fiber (SSMF). This work paves the way for further study of the application of the Co-GRU structure for both the data-driven channel modeling and the decoder performance improvement in E2E design.

[1]  S. Turitsyn,et al.  Memory-aware end-to-end learning of channel distortions in optical coherent communications. , 2022, Optics Express.

[2]  Francesco Da Ros,et al.  Recent advances in constellation optimization for fiber-optic channels , 2022, 2022 European Conference on Optical Communication (ECOC).

[3]  Lixia Xi,et al.  Complex Long Short-Term Memory Neural Networks for Fiber Nonlinearity Equalization in Long-Haul Transmission Systems , 2022, 2022 20th International Conference on Optical Communications and Networks (ICOCN).

[4]  Weisheng Hu,et al.  End-to-End Deep Learning for Long-haul Fiber Transmission Using Differentiable Surrogate Channel , 2022, Journal of Lightwave Technology.

[5]  Vahid Aref,et al.  End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping , 2021, 2022 Optical Fiber Communications Conference and Exhibition (OFC).

[6]  Francesco Da Ros,et al.  End-to-End Learning of a Constellation Shape Robust to Channel Condition Uncertainties , 2021, Journal of Lightwave Technology.

[7]  Lei Zhang,et al.  Ultralow Complexity Long Short-Term Memory Network for Fiber Nonlinearity Mitigation in Coherent Optical Communication Systems , 2021, Journal of Lightwave Technology.

[8]  Weisheng Hu,et al.  An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization , 2021, Journal of Lightwave Technology.

[9]  X. Xin,et al.  Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system. , 2021, Optics express.

[10]  P. Bayvel,et al.  End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design , 2020, 2020 European Conference on Optical Communications (ECOC).

[11]  Mingliang Li,et al.  End-to-end Learning for Optical Fiber Communication with Data-driven Channel Model , 2020, 2020 Opto-Electronics and Communications Conference (OECC).

[12]  Simone Gaiarin,et al.  End-to-End Optimization of Coherent Optical Communications Over the Split-Step Fourier Method Guided by the Nonlinear Fourier Transform Theory , 2020, Journal of Lightwave Technology.

[13]  Hua Feng,et al.  Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection , 2020 .

[14]  Hartmut Hafermann,et al.  Analysis and Experimental Demonstration of Orthant-Symmetric Four-Dimensional 7 bit/4D-Sym Modulation for Optical Fiber Communication , 2020, Journal of Lightwave Technology.

[15]  Bin Chen,et al.  End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information , 2019, 2020 Optical Fiber Communications Conference and Exhibition (OFC).

[16]  Fayçal Ait Aoudia,et al.  Trainable Communication Systems: Concepts and Prototype , 2019, IEEE Transactions on Communications.

[17]  Nandana Rajatheva,et al.  Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[18]  Xudong Wang,et al.  A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems , 2019, IEEE Access.

[19]  Metodi P. Yankov,et al.  End-to-end Learning for GMI Optimized Geometric Constellation Shape , 2019, 45th European Conference on Optical Communication (ECOC 2019).

[20]  Polina Bayvel,et al.  End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks , 2019, Optics express.

[21]  Benjamin J. Puttnam,et al.  Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning , 2018, ArXiv.

[22]  Vishnu Raj,et al.  Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models , 2018, IEEE Communications Letters.

[23]  Polina Bayvel,et al.  End-to-End Deep Learning of Optical Fiber Communications , 2018, Journal of Lightwave Technology.

[24]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[25]  Alex Alvarado,et al.  Achievable Information Rates for Fiber Optics: Applications and Computations , 2017, Journal of Lightwave Technology.

[26]  Stephan ten Brink,et al.  Deep Learning Based Communication Over the Air , 2017, IEEE Journal of Selected Topics in Signal Processing.

[27]  Y. Jiang,et al.  Recent Advances in the Modeling of the Impact of Nonlinear Fiber Propagation Effects on Uncompensated Coherent Transmission Systems , 2017, Journal of Lightwave Technology.

[28]  Frank Hutter,et al.  SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.

[29]  Georg Böcherer,et al.  On Probabilistic Shaping of Quadrature Amplitude Modulation for the Nonlinear Fiber Channel , 2016, Journal of Lightwave Technology.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[32]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Gabriella Bosco,et al.  EGN model of non-linear fiber propagation. , 2014, Optics express.

[34]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[35]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[36]  A. Krizhevsky.  ImageNet Classification with Deep Convolutional Neural Networks , 2022 .