End-to-End Learning for Chromatic Dispersion Compensation in Optical Fiber Communication

被引：6
|
作者：
Li, Mingyu [1 ]
Wang, Shaowei [1 ]
机构：
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China
来源：
IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Generative adversarial networks; Optical receivers; Optical transmitters; Optical pulses; Generators; Training; Signal to noise ratio; Chromatic dispersion compensation; end-to-end learning; generative adversarial network; optical fiber communication;
D O I：
10.1109/LCOMM.2022.3175254
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
In this Letter, we investigate the chromatic dispersion compensation problem in optical fiber communication. An end-to-end autoencoder (AE) is proposed to replace the transceiver of the traditional intensity modulation direct detection system. To deal with the obstructed gradient return problem in end-to-end transmission, we introduce a generative adversarial network to simulate the channel transmission process and employ a square-law detector for incoherent detection to reduce the complexity. Simulation results show that the BER of the proposed system can be significantly cut down compared with the conventional electric domain compensation algorithms.
引用
收藏
页码：1829 / 1832
页数：4
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