Autoencoder-Based Optical Wireless Communications Systems
Autoencoder-Based Optical Wireless Communications Systems
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DOI：
10.1109/glocomw.2018.8644104
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发表时间：
2018-12
期刊：
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通讯作者：
Morteza Soltani;Wael Fatnassi;Ahmed Aboutaleb;Z. Rezki;Arupjyoti Bhuyan;Paul Titus
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文献类型：
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作者：
Morteza Soltani;Wael Fatnassi;Ahmed Aboutaleb;Z. Rezki;Arupjyoti Bhuyan;Paul Titus
在这项研究中，我们提出了深度神经网络自编码器，用于捕获单用户和多用户光无线通信（OWC）系统的端到端性能。我们比较了端到端的性能的建议自动编码器（基于学习的OWC系统）与最先进的基于模型的OWC系统的误块率（BLER）度量。我们的数值结果表明，建议的基于学习的OWC系统优于基于模型的同行在单用户和多用户设置。
In this study, we propose deep neural network autoencoders for capturing the end-to-end performance of the single- and multi-user optical wireless communications (OWC) systems. We compare the end-to-end performance of the proposed autoencoders (learning-based OWC systems) with the state-of-the art model-based OWC systems in terms of the block error rate (BLER) metric. Our numerical results indicate that the proposed learning-based OWC system outperforms the model-based counterparts in both single- and multi-user settings.