End-to-end learning strategy with channel-aided polar autoencoder in IM/DD optical interconnection

被引:0
|
作者
Mu, Yujia [1 ]
He, Hailian [1 ]
Ming, Jun [1 ]
Song, Junyuan [1 ]
Xu, Qi [1 ]
Gao, Ran [1 ]
Li, Zhipei [1 ]
Xin, Xiangjun [1 ]
Dong, Ze [1 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
来源
OPTICS EXPRESS | 2024年 / 32卷 / 22期
基金
中国国家自然科学基金;
关键词
D O I
10.1364/OE.538887
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
An end-to-end (E2E) learning strategy with a channel-aided polar autoencoder (AE) is proposed and experimentally demonstrated in an intensity-modulation direct-detection optical interconnection system. With the global training of the E2E deep neural network, the learned fiber channel and transceiver characteristics are used to back-forward aid the channel indexes of AE, thus obtaining optimal polarization weight in polar encoding to mitigate signal impairments. The experimental results reveal that when the 50 GBaud PAM4 signals are transmitted over standard single mode fiber, the proposed polar AE by E2E learning strategy can effectively improve the received power sensitivity by 1.5 dB under conditions of overall adaptation of system parameters optimization. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:39727 / 39733
页数:7
相关论文
共 50 条
  • [21] End-to-end entanglement in a coherent feedback interconnection of three nondegenerate optical parametric amplifiers
    Shi, Zhan
    Nurdin, Hendra I.
    2014 4TH AUSTRALIAN CONTROL CONFERENCE (AUCC), 2014, : 89 - 94
  • [22] Performance investigation of polar coded IM/DD optical OFDM for short reach interconnection
    Fang, Jiafei
    Xiao, Shilin
    Liu, Ling
    Bi, Meihua
    Zhang, Lu
    Zhang, Yunhao
    Hu, Weisheng
    2017 OPTO-ELECTRONICS AND COMMUNICATIONS CONFERENCE (OECC) AND PHOTONICS GLOBAL CONFERENCE (PGC), 2017,
  • [23] An improved deep learning-based end-to-end autoencoder for UAV-to-ground free space optical communication
    Zhang, Qianwu
    Chen, Guanwen
    Liu, Boyang
    Zhi, Xuzhuang
    Zhan, Shucheng
    Zhang, Jing
    Cao, Bingyao
    Li, Zhengxuan
    OPTICS COMMUNICATIONS, 2023, 549
  • [24] Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems
    Almaeen, Manal
    Alanazi, Yasir
    Sato, Nobuo
    Melnitchouk, W.
    Kuchera, Michelle P.
    Li, Yaohang
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [25] Deep learning based end-to-end visible light communication with an in-band channel modeling strategy
    Li, Zhongya
    Shi, Jianyang
    Zhao, Yiheng
    Li, Guoqiang
    Chen, Jiang
    Zhang, Junwen
    Chi, Nan
    OPTICS EXPRESS, 2022, 30 (16) : 28905 - 28921
  • [26] Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
    Li, Shen
    Hager, Christian
    Garcia, Nil
    Wymeersch, Henk
    2018 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC), 2018,
  • [27] A Multi-Rate Approach for Nonlinear Pre-Distortion Using End-to-End Deep Learning in IM-DD Systems
    Minelli, Leonardo
    Forghieri, Fabrizio
    Nespola, Antonino
    Straullu, Stefano
    Gaudino, Roberto
    JOURNAL OF LIGHTWAVE TECHNOLOGY, 2023, 41 (02) : 420 - 431
  • [28] Nonlinear Pre-distortion through a Multi-rate End-to-end Learning Approach over VCSEL-MMF IM-DD Optical Links
    Minelli, Leonardo
    Forghieri, Fabrizio
    Gaudino, Roberto
    2022 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC), 2022,
  • [29] Multimodal Sensory Data Fusion-aided Channel modelling: An End-to-End Approach
    Peng, Bifeng
    Ma, Nan
    Zhang, Kaiheng
    Peng, Ke
    Chen, Jianqiao
    2024 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC, 2024,
  • [30] Model-Aware End-to-End Learning for SISO Optical Wireless Communication Over Poisson Channel
    Si-Ma, Ling-Han
    Zhu, Zhao-Rui
    Yu, Hong-Yi
    IEEE PHOTONICS JOURNAL, 2020, 12 (06):