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    A novel digital frequency offset (DFO) loading technique combined with a neural network (NN) receiver enhances discrete spectrum nonlinear frequency division multiplexing (DS-NFDM) systems. This DFO-NN system significantly reduces amplified spontaneous emission (ASE) noise and processing errors, improving transmission performance.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning in Telecommunications

    Background:

    • Amplified spontaneous emission (ASE) noise and processing errors degrade signal integrity in discrete spectrum nonlinear frequency division multiplexing (DS-NFDM) systems.
    • Traditional nonlinear Fourier transform (NFT) algorithms and frequency offset estimation (FOE) methods struggle with noise and computational complexity in high-power DS-NFDM transmission.

    Purpose of the Study:

    • To propose and validate an innovative DS-NFDM system, termed DFO-NN, that integrates digital frequency offset (DFO) loading with a neural network (NN) receiver.
    • To mitigate ASE noise and suppress processing noise, thereby enhancing signal quality and transmission distance in DS-NFDM systems.

    Main Methods:

    • Implemented a DFO loading technique at the transmitter, leveraging the mapping between information bits and eigenvalue offsets to counteract ASE noise.
    • Employed an optimized NN-based receiver to learn the periodicity of DFO-loaded DS-NFDM waveforms, replacing conventional NFT and FOE processes.
    • Verified the system's performance through numerical simulations and experimental setups at 1 GBaud and 2 GBaud.

    Main Results:

    • The DFO-NN system demonstrated a wide frequency offset tolerance, ranging from -5.8 GHz to +5.3 GHz.
    • Achieved significant reductions in optical signal-to-noise ratio (OSNR) penalties: 6.0 dB compared to b-NFT and 2.0 dB compared to DFO-NFT systems.
    • Extended the maximum transmission distance by 473 km compared to the DFO-NFT system, while maintaining comparable computational complexity (O(M^2)).

    Conclusions:

    • The proposed DFO-NN system effectively suppresses ASE noise and processing errors in DS-NFDM systems.
    • The NN-based receiver offers a robust and efficient alternative to traditional NFT and FOE algorithms for DS-NFDM signal demodulation.
    • The DFO-NN system presents a promising solution for extending the reach and improving the performance of high-power fiber optic communication systems.