Machine-Learning Based Equalizers for Mitigating the Interference in Asynchronous MIMO OWC Systems

doi:10.1109/JLT.2021.3057396

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	Machine-Learning Based Equalizers for Mitigating the Interference in Asynchronous MIMO OWC Systems
	Y. Li; T. Geng; R. Tian and S. Gao
	2021
发表期刊	Journal of Lightwave Technology
ISSN	7338724
卷号	39 期号:9 页码:2800-2808
摘要	The error performance of the optical wireless communication (OWC) link suffers from the effects of atmospheric turbulence and pointing errors. The multi-input-multi-output (MIMO) system can combat the damage by transmitting diverse replicas of symbols to the receivers, i.e. the spatial diversity. However, different delays between the transceivers can introduce inter-symbol interference (ISI) which degrades the system performance. The delays during transmission are mainly caused by placing locations and optical path differences. This article proposes algorithms to mitigate the ISI based on machine learning techniques, including both neural networks and the genetic algorithm. In the case of multi-input-single-output (MISO) system, we propose an algorithm based on a bidirectional long short-term memory (LSTM) recurrent neural network (RNN), which works as an equalizer. In the case of single-input-multiple-output (SIMO) system, additional delayers are utilized to align the signals in different apertures. The problem of deducing the values of the delayers is considered as seeking the minimum value in a high-dimensional space. With the help of the genetic algorithm, optimal values of the delayers are maintained, which is named as GAD (genetic algorithm-based delayers). In a more general MIMO case, the GAD and LSTM equalizers are further combined to deal with the ISI issue in the asynchronous MIMO OWC systems, (i.e. GAD-LSTM). Both experimental and simulation results show the remarkable performance improvement of the proposed method over conventional methods. 1983-2012 IEEE.
DOI	10.1109/JLT.2021.3057396
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65398
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Li,T. Geng,R. Tian and S. Gao. Machine-Learning Based Equalizers for Mitigating the Interference in Asynchronous MIMO OWC Systems[J]. Journal of Lightwave Technology,2021,39(9):2800-2808.
APA	Y. Li,T. Geng,&R. Tian and S. Gao.(2021).Machine-Learning Based Equalizers for Mitigating the Interference in Asynchronous MIMO OWC Systems.Journal of Lightwave Technology,39(9),2800-2808.
MLA	Y. Li,et al."Machine-Learning Based Equalizers for Mitigating the Interference in Asynchronous MIMO OWC Systems".Journal of Lightwave Technology 39.9(2021):2800-2808.

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