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Memory-augmented neural network equalizer for short-reach optical interconnects.

Zedi Zhang, Zhang Luo, Mingche Lai

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    |May 4, 2026
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    Summary
    This summary is machine-generated.

    A novel memory-augmented neural network equalizer (MANNE) effectively combats intersymbol interference in high-speed optical systems. This advanced equalizer improves receiver sensitivity and reduces computational complexity for 4-level pulse amplitude modulation (PAM-4) signals.

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    Area of Science:

    • Optical Communications Engineering
    • Machine Learning for Signal Processing
    • Advanced Modulation Techniques

    Background:

    • High-speed optical transmission systems face significant challenges from intersymbol interference (ISI).
    • Traditional equalizers often struggle to effectively mitigate complex channel impairments in modern optical networks.
    • Existing neural network equalizers (NNEs) can be limited in their ability to dynamically adapt to varying signal conditions.

    Purpose of the Study:

    • To introduce a novel Memory-Augmented Neural Network Equalizer (MANNE) for mitigating ISI in high-speed optical systems.
    • To enhance the feature representation and generalization capabilities of neural network equalizers through external memory.
    • To demonstrate the practical effectiveness of MANNE in real-world optical transmission experiments.

    Main Methods:

    • Development of MANNE by augmenting a traditional NNE with a key-value based external memory module.
    • Implementation of a memory feedback mechanism for improved training and feature representation.
    • Experimental validation using a 128 Gbaud 4-level pulse amplitude modulation (PAM-4) signal over a 2 km standard single-mode fiber link.

    Main Results:

    • MANNE achieved approximately 1.0 dB improvement in receiver sensitivity compared to traditional NNE at the KP4 forward error correction (KP4-FEC) threshold.
    • MANNE successfully met the KP4-FEC threshold, outperforming the Volterra nonlinear equalizer (VNLE) which failed to reach it.
    • MANNE reduced computational complexity by 72.2% compared to VNLE, demonstrating superior performance and efficiency.

    Conclusions:

    • The proposed MANNE offers a significant advancement in mitigating ISI for high-speed optical transmission.
    • MANNE provides a robust and computationally efficient solution for PAM-4 signal equalization.
    • This approach balances high performance with reduced complexity, making it suitable for practical optical communication systems.