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End-to-End Optimized Transmission over Dispersive Intensity-modulated Channels Using Bidirectional Recurrent Neural Networks

Karanov, Boris; Lavery, Domaniç; Bayvel, Polina; Schmalen, Laurent

Abstract (englisch):
We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84 Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel.

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Verlagsausgabe §
DOI: 10.5445/IR/1000096544
Veröffentlicht am 16.07.2019
Originalveröffentlichung
DOI: 10.1364/OE.27.019650
Scopus
Zitationen: 24
Web of Science
Zitationen: 16
Dimensions
Zitationen: 24
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nachrichtentechnik - Communications Engineering Lab (CEL)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1094-4087
KITopen-ID: 1000096544
Erschienen in Optics express
Verlag The Optical Society of America (OSA)
Band 27
Heft 14
Seiten 19650–19663
Nachgewiesen in Dimensions
Web of Science
Scopus
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