MIMO detection using a deep learning neural network in a mode division multiplexing optical transmission system

被引：17
|
作者：
Poudel, Bishal [1 ]
Oshima, Joji [1 ]
Kobayashi, Hirokazu [1 ]
Iwashita, Katsushi [1 ]
机构：
[1] Kochi Univ Technol, Dept Photon Syst Engn, 185 Miyanokuchi, Kami City, Kochi 7828502, Japan
来源：
OPTICS COMMUNICATIONS | 2019年 / 440卷
关键词：
Multiple Input Multiple Output; Deep learning neural network; Mode division multiplexing; Optical transmission system; Optimum detector; SDM TRANSMISSION; EQUALIZATION; SIGNALS; CORE;
D O I：
10.1016/j.optcom.2019.02.016
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Optimum Multiple Input Multiple Output (MIMO) detector has always been a challenge in MIMO communication systems. In this paper, a novel MIMO detector has been designed using a supervised Deep Learning Neural Network (DLNN) and has been implemented successfully in a Mode Division Multiplexing (MDM) optical transmission system. A conventional Graded-Index Multi-Mode Fiber (GI-MMF) is used to design an MDM optical transmission system. We have used a DLNN for MIMO detection in MDM optical transmission system and have compared its performance with Zero Forcing (ZF) detector and Semi-Definite Relaxation Row-by-Row (SDR-RBR). The results confirm that our DLNN outruns the performance of traditional MIMO detectors.
引用
收藏
页码：41 / 48
页数：8
相关论文
共 50 条
  • [1] Deep-learning neural network for MIMO detection in a mode-division multiplexed optical transmission system
    Poudel, Bishal
    Oshima, Joji
    Kobayashi, Hirokazu
    Iwashita, Katsushi
    NEXT-GENERATION OPTICAL COMMUNICATION: COMPONENTS, SUB-SYSTEMS, AND SYSTEMS VIII, 2019, 10947
  • [2] Analog nonlinear MIMO receiver for optical mode division multiplexing transmission
    Spalvieri, Arnaldo
    Boffi, Pierpaolo
    Pecorino, Simone
    Barletta, Luca
    Magarini, Maurizio
    Gatto, Alberto
    Martelli, Paolo
    Martinelli, Mario
    OPTICS EXPRESS, 2013, 21 (21): : 25174 - 25183
  • [3] 16QAM Mode-Wavelength Division Multiplexing Coherent Transmission System Based on MIMO Neural Network Equalization
    Zhang Yao
    Wang Chen
    Sang Bohan
    Zhang Yu
    Wang Xinyi
    Zhou Wen
    Yu Jianjun
    ACTA OPTICA SINICA, 2024, 44 (03)
  • [4] MIMO Equalization Technology Based on Neural Network in High-Speed IM-DD Mode Division Multiplexing Transmission System
    Zhu Z.
    Zhao M.
    Zhang Y.
    Chen J.
    Guangxue Xuebao/Acta Optica Sinica, 2021, 41 (14):
  • [5] MIMO Equalization Technology Based on Neural Network in High-Speed IM-DD Mode Division Multiplexing Transmission System
    Zhu Ziyue
    Zhao Mengxin
    Zhang Yichen
    Chen Jian
    ACTA OPTICA SINICA, 2021, 41 (14)
  • [6] Deep neural network-based intercarrier interference detection for optical spectral efficient frequency division multiplexing system
    Jiang, Guozhou
    Wu, Jintao
    Li, Mengyan
    Yang, Liu
    OPTICAL ENGINEERING, 2022, 61 (12)
  • [7] Optical MIMO Signal Processing for Direct-Detection Mode-Division Multiplexing
    Choutagunta, K.
    Arik, Sercan O.
    Moradshahi, Mehrad
    Kahn, Joseph M.
    2017 19TH INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON), 2017,
  • [8] Experimentally Research on 6-mode Division Multiplexing Optical Transmission System
    Wang, Jue
    Tian, Feng
    Wu, Tianze
    Wang, Chuxuan
    2021 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS 2021), 2021, : 1228 - 1232
  • [9] An asymmetric fiber for MIMO-free mode division multiplexing transmission
    Feng, L.
    Li, Y.
    Zeng, X.
    Li, W.
    Qiu, J.
    Hong, X.
    Zuo, Y.
    Guo, H.
    Wu, J.
    2017 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP), 2017,
  • [10] Integrated WDM-compatible optical mode division multiplexing neural network accelerator
    Yin, Ruoyu
    Xiao, Huifu
    Jiang, Yongheng
    Han, Xu
    Zhang, Pu
    Chen, Li
    Zhou, Xudong
    Yuan, Mingrui
    Ren, Guanghui
    Mitchell, Arnan
    Tian, Yonghui
    OPTICA, 2023, 10 (12): : 1709 - 1718
12345