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    A novel convolutional neural network (CNN) receiver enhances non-orthogonal multiple access (NOMA) in passive optical networks (PONs). This CNN-based approach improves performance and robustness against nonlinear distortion compared to traditional successive interference cancellation (SIC) receivers.

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    Area of Science:

    • Optical communication networks
    • Signal processing for optical networks

    Background:

    • Non-orthogonal multiple access (NOMA) offers high throughput in passive optical networks (PONs).
    • Conventional successive interference cancellation (SIC) receivers in NOMA-PONs require perfect channel information and suffer from error propagation.
    • Nonlinear distortions can degrade NOMA-PON performance.

    Purpose of the Study:

    • To propose a joint channel estimation and signal detection method for NOMA-PON using convolutional neural networks (CNNs).
    • To evaluate the performance of the proposed CNN-based receiver against traditional SIC receivers.
    • To assess the robustness of the CNN-based receiver to nonlinear distortions.

    Main Methods:

    • A convolutional neural network (CNN) is trained offline for signal demodulation in NOMA-PON.
    • The trained CNN performs joint channel estimation and signal detection for data stream recovery.
    • Experimental demonstrations compare the CNN-based receiver (Rx) with a conventional SIC-based Rx.

    Main Results:

    • The CNN-based Rx demonstrates superior performance over the SIC-based Rx in NOMA-PON.
    • Lower received optical power levels are required for the CNN-based system (e.g., 4 dB lower at a 0.16 power allocation ratio) at a bit error rate (BER) of 1×10-3 over 20 km fiber.
    • The CNN-based receiver exhibits significantly better robustness against nonlinear distortions compared to the SIC-based receiver.

    Conclusions:

    • CNN-based signal demodulation is a viable and effective method for NOMA-PON.
    • The proposed CNN receiver offers improved performance, lower power requirements, and enhanced robustness against nonlinear distortions.
    • This approach presents a promising alternative to conventional SIC receivers for future flexible and high-performance optical networks.