25 October 2016 A training-aided MIMO equalization based on matrix transformation in the space division multiplexed fiber-optic transmission system
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Proceedings Volume 10158, Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage; 101580O (2016) https://doi.org/10.1117/12.2246754
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoning Guan, Xiaoning Guan, Bo Liu, Bo Liu, Lijia Zhang, Lijia Zhang, Xiangjun Xin, Xiangjun Xin, Qinghua Tian, Qinghua Tian, Qi Zhang, Qi Zhang, Feng Tian, Feng Tian, Dengao Li, Dengao Li, Jumin Zhao, Jumin Zhao, Renfan Wang, Renfan Wang, } "A training-aided MIMO equalization based on matrix transformation in the space division multiplexed fiber-optic transmission system", Proc. SPIE 10158, Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage, 101580O (25 October 2016); doi: 10.1117/12.2246754; https://doi.org/10.1117/12.2246754
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