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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Terahertz (THz) wireless-over-fiber systems face challenges with nonlinear distortions.
    • Intensity-modulated direct-detection (IM-DD) systems require efficient methods for signal equalization.
    • Integrating fiber and wireless links at THz frequencies demands advanced signal processing techniques.

    Purpose of the Study:

    • To propose and validate a sparse deep learning method (SDLM) for mitigating nonlinear distortions in THz wireless-over-fiber systems.
    • To evaluate the performance and complexity of SDLM compared to traditional equalization methods.
    • To demonstrate the effectiveness of SDLM in a 135 GHz IM-DD communication system.

    Main Methods:

    • Development of a sparse deep learning method (SDLM) incorporating an L1-regularized cost function.
    • Experimental validation using a 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing (OFDM) signal.
    • Transmission over a 15-km single-mode fiber (SMF) and a 60 cm wireless link at 135 GHz.
    • Comparison with traditional Volterra nonlinear equalizer (VNLE), sparse VNLE (SVNLE), and conventional deep learning method (DLM).

    Main Results:

    • The proposed SDLM successfully mitigates nonlinear distortions in the THz wireless-over-fiber system.
    • SDLM achieved significant complexity reductions: 76% vs. VNLE, 72% vs. DLM, and 61% vs. SVNLE for 8-QAM.
    • The method maintained signal integrity without performance loss.

    Conclusions:

    • SDLM offers a viable and efficient solution for nonlinear distortion mitigation in THz wireless-over-fiber systems.
    • The L1-regularized deep learning approach provides superior performance and reduced complexity.
    • This technique enhances the feasibility of cost-effective IM-DD THz communication systems.