TR2020-155
End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping
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- , "End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping", European Conference on Optical Communication (ECOC), DOI: 10.1109/ECOC48923.2020.9333382, November 2020.BibTeX TR2020-155 PDF Video
- @inproceedings{Talreja2020nov,
- author = {Talreja, Veeru and Koike-Akino, Toshiaki and Wang, Ye and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
- title = {End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping},
- booktitle = {European Conference on Optical Communication (ECOC)},
- year = 2020,
- month = nov,
- publisher = {IEEE},
- doi = {10.1109/ECOC48923.2020.9333382},
- isbn = {978-1-7281-7361-0},
- url = {https://www.merl.com/publications/TR2020-155}
- }
- , "End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping", European Conference on Optical Communication (ECOC), DOI: 10.1109/ECOC48923.2020.9333382, November 2020.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Communications, Multi-Physical Modeling, Optimization, Signal Processing
Abstract:
We propose an end-to-end deep learning model for phase noise-robust optical communications. A convolutional embedding layer is integrated with a deep autoencoder for multi-dimensional constellation design to achieve shaping gain. The proposed model offers a significant gain up to 2 dB.