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Enhancing nonlinear compensation efficiency with multi-task neural networks for coherent optical systems.

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    We introduce a low-complexity nonlinear compensation method using a multi-task neural network (MT-NN) for optical communication systems. This approach reduces computational load while maintaining high performance, offering an efficient solution.

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    Area of Science:

    • Optical communication systems engineering
    • Computational intelligence in telecommunications
    • Signal processing for optical networks

    Background:

    • Traditional nonlinear compensation methods in high-speed optical systems suffer from high computational complexity.
    • Existing techniques struggle to balance compensation accuracy with computational efficiency.

    Purpose of the Study:

    • To develop a low-complexity nonlinear compensation method for coherent optical communication systems.
    • To reduce computational overhead without sacrificing system performance.

    Main Methods:

    • A multi-task neural network (MT-NN) framework is proposed, leveraging shared network weights for simultaneous symbol processing.
    • Complexity-aware mean square error (MSE) and partial grid search optimization are employed.
    • Transfer learning (TL) is integrated to improve training efficiency.

    Main Results:

    • The MT-NN approach significantly lowers computational complexity across various optical transmission scenarios.
    • Compensation accuracy is maintained, demonstrating effectiveness without performance compromise.
    • Experimental results show a superior accuracy-efficiency trade-off compared to single-task neural networks (ST-NN).

    Conclusions:

    • The proposed MT-NN-based nonlinear compensation offers a practical and efficient solution for next-generation optical communication systems.
    • This method addresses the challenge of computational complexity in high-speed optical networks.
    • The study highlights the potential of multi-task learning and transfer learning for advanced optical communication signal processing.