IM/DD mode division multiplexing transmission enabled by machine learning-based linear and nonlinear MIMO equalization

Abstract We demonstrate two mode division multiplexing transmission systems through few-mode fiber by using two degenerate LP11 modes in high speed and low cost optical interconnection scenarios. Two novel MIMO equalizers based on machine learning are proposed and utilized to eliminate the impairments. In the ultra-low cost optical interconnection, the linear equalizer based on the elastic net can effectively reduce the bit error rate to the hard-decision forward error correction threshold with an extremely fast convergence speed. With the help of the nonlinear equalizer based on the neural network, high speed short-reach transmission has been achieved. By multiplexing two degenerate modes with the bit rate of 100 Gb/s/ λ , 2 × 100 Gb/s transmission of 30 G-class 1550 nm optical devices has been realized. Without the optical amplifier, the receiver sensitivity could be around -11 dBm in 2 × 50 Gb/s transmission. This provides potential solutions for the future evolution of short-reach optical links.

[1]  Weisheng Hu,et al.  Unsupervised Learning for Neural Network-Based Blind Equalization , 2020, IEEE Photonics Technology Letters.

[2]  Zhaopeng Xu,et al.  Cascade recurrent neural network-assisted nonlinear equalization for a 100  Gb/s PAM4 short-reach direct detection system. , 2020, Optics letters.

[3]  Gang-Ding Peng,et al.  Mode-division multiplexed transmission with inline few-mode fiber amplifier. , 2012, Optics express.

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  L. Nelson,et al.  Space-division multiplexing in optical fibres , 2013, Nature Photonics.

[6]  Guifang Li,et al.  Experimental demonstration of adaptive frequency-domain equalization for mode-division multiplexed transmission , 2013, 2013 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC).

[7]  Yann Frignac,et al.  Understanding discrete linear mode coupling in few-mode fiber transmission systems , 2011, 2011 37th European Conference and Exhibition on Optical Communication.

[8]  A. Lobato,et al.  Impact of mode coupling on the mode-dependent loss tolerance in few-mode fiber transmission , 2012, 2012 38th European Conference and Exhibition on Optical Communications.

[9]  Xin Zhang,et al.  Adaptive blind equalization algorithm using a decision feedback recurrent neural network in mode-division multiplex systems , 2014, 2014 OptoElectronics and Communication Conference and Australian Conference on Optical Fibre Technology.

[10]  Yuta Otsuka,et al.  Computational-Complexity Comparison of Artificial Neural Network and Volterra Series Transfer Function for Optical Nonlinearity Compensation with Time- and Frequency-Domain Dispersion Equalization , 2018, 2018 European Conference on Optical Communication (ECOC).

[11]  Katsushi Iwashita,et al.  MIMO detection using a deep learning neural network in a mode division multiplexing optical transmission system , 2019, Optics Communications.

[12]  Chigo Okonkwo,et al.  Compact spatial multiplexers for mode division multiplexing. , 2014, Optics express.

[13]  Yingchun Li,et al.  Decoding of 10-G Optics-Based 50-Gb/s PAM-4 Signal Using Simplified MLSE , 2018, IEEE Photonics Journal.

[14]  Lei Yue,et al.  Recent Advances in Equalization Technologies for Short-Reach Optical Links Based on PAM4 Modulation: A Review , 2019, Applied Sciences.

[15]  K. Qiu,et al.  Experimental demonstration of single-sideband, four-level pulse amplitude modulation/direct-detection degenerate mode-division multiplexing transmission based on multiple-input, multiple-output nonlinear equalizer. , 2020, Applied optics.

[16]  Jian Chen,et al.  137 Gb/s PAM-4 Transmissions at 850 nm over 40 cm Optical Backplane with 25 G Devices with Improved Neural Network-Based Equalization , 2019, Applied Sciences.

[17]  G. Ng,et al.  Experimental Demonstration of Thermally Tunable Fano and EIT Resonances in Coupled Resonant System on SOI Platform , 2018, IEEE Photonics Journal.

[18]  Guijun Hu,et al.  A Variable Step-Size Unconstrained Adaptive FD-LMS Algorithm for MDM Transmission , 2018, IEEE Photonics Journal.

[19]  G. Bosco,et al.  Recent Progress and Fundamental Limitations of Optical MLSE Receivers , 2007, 2007 9th International Conference on Transparent Optical Networks.

[20]  Guifang Li,et al.  Adaptive Frequency-Domain Equalization for Mode-Division Multiplexed Transmission , 2012 .

[21]  Keang-Po Ho,et al.  Mode coupling effects in multi-mode fibers , 2012, OFC/NFOEC.

[22]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[23]  Weisheng Hu,et al.  Machine Learning for 100 Gb/s/λ Passive Optical Network , 2019, Journal of Lightwave Technology.

[24]  Yusuke Sasaki,et al.  Monolithic mode-selective few-mode multicore fiber multiplexers , 2017, Scientific Reports.

[25]  Ting Wang,et al.  Experimental demonstration of adaptive recursive least square frequency-domain equalization for long-distance mode-division multiplexed transmission , 2015, 2015 European Conference on Optical Communication (ECOC).