400 Gbit/s 4 mode transmission for IM/DD OAM mode division multiplexing optical fiber communication with a few-shot learning-based AffinityNet nonlinear equalizer

Author:

Wang FeiORCID,Gao Ran,Li Zhipei,Liu Jie1,Cui Yi234,Xu Qi,Pan Xiaolong,Zhu LeiORCID,Wang Fu234ORCID,Guo DongORCID,Chang Huan,Zhou Sitong,Dong ZeORCID,Zhang Qi234,Tian Qinghua234ORCID,Tian Feng234,Huang Xin5,Yan Jinghao5,Jiang Lin5,Xin Xiangjun

Affiliation:

1. Sun Yat-Sen University

2. Beijing University of Posts and Telecommunications (BUPT)

3. Beijing Key Laboratory of Space-Ground Interconnection and Convergence

4. State Key Laboratory of Information Photonics and Optical Communications

5. Ultra-high Speed Communication Laboratory

Abstract

Nonlinear impairment in a high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication system presents high complexity and strong stochasticity due to the massive optoelectronic devices. In this paper, we propose an Affinity Network (AffinityNet) nonlinear equalizer for an OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with four OAM modes. The labeled training and testing signals from the OAM-MDM system can be regarded as “small sample” and “large target”, respectively. AffinityNet can be used to build an accurate nonlinear model using “small sample” based on few-shot learning and can predict the stochastic characteristic nonlinearity of OAM-MDM with a high level of generalization. As a result, the AffinityNet nonlinear equalizer can effectively compensate the stochastic nonlinearity in the OAM-MDM system, despite the large difference between the training and testing signals due to the stochastic nonlinear impairment. An experiment was conducted on a 400 Gbit/s transmission with four OAM modes using a pulse amplitude modulation-8 (PAM-8) signal over a 2 km ring-core fiber (RCF). Our experimental results show that the proposed nonlinear equalizer outperformed the conventional Volterra equalizer with improvements in receiver sensitivity of 1.7, 1.8, 3, and 3.3 dB for the four OAM modes at the 15% forward error correction (FEC) threshold, respectively. In addition, the proposed equalizer outperformed a convolutional neural network (CNN) equalizer with improvements in receiver sensitivity of 0.8, 0.5, 0.9, and 1.4 dB for the four OAM modes at the 15% FEC threshold. In the experiment, a complexity reduction of 37% and 83% of the AffinityNet equalizer is taken compared to the conventional Volterra equalizer and CNN equalizer, respectively. The proposed equalizer is a promising candidate for a high-speed OAM-MDM optical fiber communication system.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Beijing Municipal Natural Science Foundation

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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