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
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