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Binomial training algorithm for neuromorphic photonics applied to channel equalization.

Luís C B Silva, Mateus S Coelho, Pablo R N Marciano

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    We introduce a novel photonic neural network using binomial training for optical channel equalization. This system significantly improves signal quality in fiber optic transmissions, demonstrating robust performance and generalization capabilities.

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    Area of Science:

    • Photonics
    • Optical Communications
    • Artificial Intelligence

    Background:

    • Optical communication systems face signal degradation over long distances.
    • Channel equalization is crucial for maintaining signal integrity.
    • Existing equalization methods can be complex and computationally intensive.

    Purpose of the Study:

    • To propose and evaluate a novel photonic time-delayed neural network for channel equalization.
    • To demonstrate the effectiveness of a binomial training algorithm for this optical neural network.
    • To assess the performance and generalization capabilities of the developed system.

    Main Methods:

    • Development of a photonic time-delayed neural network architecture using delay lines, phase, and amplitude modulators.
    • Numerical implementation and training using experimentally acquired data from a 10 Gb/s optical transmission system.
    • Evaluation of bit error rate (BER) and eye diagram improvements after equalization.

    Main Results:

    • Significant improvements in BER and eye diagram for 10 Gb/s on-off keying (OOK) signals over 100 km of single-mode fiber.
    • The photonic neural network successfully restored highly degraded signals, opening closed eye diagrams.
    • 93.3% of transmitted bit sequences achieved performance below the forward error correction (FEC) limit, indicating strong generalization.

    Conclusions:

    • The proposed photonic time-delayed neural network with binomial training offers an efficient solution for optical channel equalization.
    • The discrete, hardware-friendly optimization is suitable for photonic integrated circuits.
    • The system demonstrates excellent performance and generalization for real-world optical transmission challenges.