Karanov, B;
Lavery, D;
Bayvel, P;
Schmalen, L;
(2019)
End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks.
Optics Express
, 27
(14)
pp. 19650-19663.
10.1364/OE.27.019650.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/OE.27.019650 |
| Publisher version: | https://doi.org/10.1364/OE.27.019650 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10077629 |
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