@inproceedings{ccbdd2d785ed44e68192703cc3a22bea,
title = "A training-aided MIMO equalization based on matrix transformation in the space division multiplexed fiber-optic transmission system",
abstract = "A novel training sequence is designed for the space division multiplexed fiber-optic transmission system in this paper. The training block is consisting of segmented sequence, which can be used to compensate time offset and distortion (such as dispersion) in the transmission link. The channel function can be obtained by one tap equalization in the receiver side. This paper designs the training sequence by adjusting the length of the training signals and implementing matrix transformation, to obtain the coefficient of equalizer for channel detect and equalization. This new training sequence reduces system complexity and improves transmission efficiency at the same time. Compared with blind equalization, the matrix transformation based training sequence can reduce system complexity, and perform targeted equalization to the mechanism of mode coupling in the space division optical fiber system. As a result, it can effectively improve signal transmission quality and reduce bit error rate.",
keywords = "MIMO, SDM, equalization, matrix transformation, multi-channel, multicore, multimode, multiplexed, optical transmission, training sequence",
author = "Xiaoning Guan and Bo Liu and Lijia Zhang and Xiangjun Xin and Qinghua Tian and Qi Zhang and Feng Tian and Dengao Li and Jumin Zhao and Renfan Wang",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; International Symposium on Optical Communication and Optical Fiber Sensors and the International Symposium on Optical Memories for Big Data Storage ; Conference date: 09-05-2016 Through 11-05-2016",
year = "2016",
doi = "10.1117/12.2246754",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xiaodi Tan and Yanbiao Liao",
booktitle = "Optical Communication and Optical Fiber Sensors and Optical Memories for Big Data Storage",
address = "United States",
}