Bi-GRU Enhanced Cost-Effective Memory-Aware End-to-End Learning for Geometric Constellation Shaping in Optical Coherent Communications

We propose a cost-effective and memory-aware end-to-end learning scheme utilizing bi-directional gated recurrent unit (bi-GRU) for geometric constellation shaping (GCS) under the first-order regular perturbation (FRP) auxiliary channel. The performance of the proposed system has been numerically ver...

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Bibliographic Details
Published in:IEEE photonics journal 2024-02, Vol.16 (1), p.1-10
Main Authors: Liu, Zhiyang, Liu, Xiaoyu, Xiao, Shilin, Yang, Weiying, Hu, Weisheng
Format: Article
Language:English
Subjects:
Bi-directional gated recurrent unit
Communications systems
Complexity theory
end-to-end learning
Fiber nonlinear optics
geometric constellation shaping
Learning
Logic gates
Long short term memory
Optical fiber communication
Optical fibers
Quadrature amplitude modulation
Recurrent neural networks
Wavelength division multiplexing
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Summary:We propose a cost-effective and memory-aware end-to-end learning scheme utilizing bi-directional gated recurrent unit (bi-GRU) for geometric constellation shaping (GCS) under the first-order regular perturbation (FRP) auxiliary channel. The performance of the proposed system has been numerically verified at a 32 GBd 5-channel wavelength division multiplexing (WDM) 64 quadrature amplitude modulation (QAM) 800 km optical coherent communication system. Results show that the proposed bi-GRU based GCS scheme can achieve a performance gain over square 64QAM in mutual information (MI) with 0.12 bits/symbol and a Q-factor gain of 0.4 dB at optimal launched optical power. When transmission distance is extended to 1280 km, a generalized mutual information (GMI) gain of 0.136 bits/symbol is observed. Additionally, compared with the bi-directional long short-term memory (bi-LSTM) based GCS, the proposed bi-GRU scheme has lower computation complexity with similar system performance.
ISSN:1943-0655
1943-0655
1943-0647
DOI:10.1109/JPHOT.2023.3344184