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SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON.

Lei Xue, Lilin Yi, Rui Lin

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    A novel neural network (NN) equalizer effectively mitigates the semiconductor optical amplifier (SOA) pattern effect in 50G passive optical networks (PONs). This advancement significantly enhances receiver dynamic range and simplifies digital signal processing for improved optical communication systems.

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    Area of Science:

    • Optical communications
    • Nonlinear optics
    • Machine learning applications

    Background:

    • Semiconductor optical amplifiers (SOAs) are crucial for O-band power amplification in passive optical networks (PONs) due to their efficiency and integration capabilities.
    • Nonlinear pattern effects in SOAs degrade system performance, particularly when used as pre-amplifiers.
    • Existing pattern effect mitigation techniques lack simplicity and flexibility for practical implementation.

    Purpose of the Study:

    • To propose and experimentally validate a neural network (NN)-based equalizer for mitigating the SOA nonlinear pattern effect in 50G PON systems.
    • To demonstrate the effectiveness of the NN equalizer in improving system performance and receiver dynamic range.
    • To explore the potential of deploying the NN model at the transmitter for simplified digital signal processing.

    Main Methods:

    • Implementation of a neural network (NN) equalizer for real-time impairment compensation.
    • Experimental setup for 50G PON using intensity modulation and direct detection with an SOA.
    • Performance evaluation based on receiver dynamic range and forward error correction (FEC) limits.

    Main Results:

    • The NN-based equalizer effectively mitigated the SOA nonlinear pattern effect.
    • A significant improvement in receiver dynamic range was achieved, reaching a 29-dB power budget.
    • The NN model demonstrated successful pre-equalization when placed at the transmitter.

    Conclusions:

    • The proposed NN equalizer offers a powerful and flexible solution for SOA pattern effect mitigation in advanced PONs.
    • This approach enhances system performance and simplifies receiver-side digital signal processing.
    • The transmitter-side deployment of the NN model presents a promising strategy for future optical network design.