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Complex principal component analysis-based complex-valued fully connected NN equalizer for optical fibre

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    A new complex principal component analysis-based complex-valued fully connected neural network (P-CFNN) effectively mitigates Kerr nonlinearity in optical fibers. This advanced model significantly reduces computational complexity while enhancing transmission capacity.

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    Area of Science:

    • Optical Communications
    • Nonlinear Optics
    • Machine Learning in Photonics

    Background:

    • Kerr nonlinearity in optical fibers limits transmission capacity.
    • Existing mitigation schemes require further optimization for efficiency.

    Purpose of the Study:

    • To propose and experimentally validate a novel complex principal component analysis-based complex-valued fully connected neural network (P-CFNN) for mitigating Kerr nonlinearity.
    • To reduce the computational complexity of nonlinear compensation algorithms in optical communication systems.

    Main Methods:

    • Development of a novel complex principal component analysis (CPCA) algorithm integrated into a complex-valued fully connected neural network (CFNN).
    • Experimental demonstration on a dual-polarization 64-quadrature-amplitude modulation coherent optical communication system.
    • Comparison with an equivalent real-valued fully connected neural network (RFNN) for performance evaluation.

    Main Results:

    • The P-CFNN equalizer achieved the best performance across all tested optical powers.
    • A maximum ΔQ-factor improvement of 3.94 dB was observed compared to systems without nonlinear compensation.
    • The P-CFNN demonstrated a 40% reduction in time complexity and a 70% reduction in space complexity compared to the PCA-based RFNN under equivalent Q-factor constraints.

    Conclusions:

    • The proposed P-CFNN model is highly effective in mitigating Kerr nonlinearity in optical fiber communication.
    • The P-CFNN offers significant computational advantages, proving its potential for practical applications.
    • This research advances the development of efficient nonlinear compensation techniques for high-capacity optical systems.