An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost alg...

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Detalles Bibliográficos
Autores principales: Zhang, Qianwu, Wang, Zicong, Duan, Shuaihang, Cao, Bingyao, Wu, Yating, Chen, Jian, Zhang, Hongbo, Wang, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780006/
https://www.ncbi.nlm.nih.gov/pubmed/35056196
http://dx.doi.org/10.3390/mi13010031

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780006/
https://www.ncbi.nlm.nih.gov/pubmed/35056196
http://dx.doi.org/10.3390/mi13010031

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