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    Area of Science:

    • Wireless Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Terahertz (THz) communications (252-325 GHz) are crucial for sixth-generation (6G) wireless systems.
    • Challenges like noise and nonlinearity limit THz communication efficiency.
    • Existing methods struggle to fully address these performance bottlenecks.

    Purpose of the Study:

    • To mitigate noise and nonlinearity in THz communications.
    • To improve the efficiency and reliability of THz systems.
    • To leverage deep learning and traditional signal processing for enhanced performance.

    Main Methods:

    • Development of a hybrid model integrating Convolutional Neural Networks (CNN) with Volterra filter domain knowledge.
    • Application of the proposed model to address nonlinear distortions and noise in THz signal processing.
    • Comparative analysis against traditional Volterra equalizing methods.

    Main Results:

    • Achieved a 2.28 dB improvement in total harmonic distortion (THD) compared to Volterra methods.
    • Demonstrated significant signal-to-noise ratio (SNR) improvements.
    • Validated the effectiveness of the hybrid CNN-Volterra approach.

    Conclusions:

    • The proposed hybrid CNN-Volterra model effectively enhances THz communication performance.
    • This approach offers a viable solution for overcoming key limitations in THz systems.
    • The findings pave the way for more reliable and advanced wireless applications.