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Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.

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    We introduce the Benveniste-Goursat input-output decision (BG-IOD) algorithm for neural network equalizers. This novel blind learning method improves convergence and symbol error rate performance in nonlinear channel equalization.

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    Area of Science:

    • Signal Processing
    • Machine Learning
    • Communications Engineering

    Background:

    • Nonlinear channel equalization is crucial for reliable data transmission.
    • Conventional blind learning algorithms face challenges with convergence and performance in nonlinear environments.
    • Neural network-based equalizers offer potential but require effective training algorithms.

    Purpose of the Study:

    • To propose a novel blind learning algorithm, BG-IOD, for enhanced neural network equalizer performance.
    • To improve convergence speed and symbol error rate (SER) in nonlinear channel equalization.
    • To leverage input decision information to overcome limitations of output-only based algorithms.

    Main Methods:

    • Development of the Benveniste-Goursat input-output decision (BG-IOD) algorithm.
    • Exploitation of input decision information alongside output information for parameter updates.
    • Application of BG soft-switching to integrate input and output decision merits.
    • Theoretical proof of improved SER convergence capability with input decision information.

    Main Results:

    • The BG-IOD algorithm demonstrates superior convergence performance compared to conventional methods.
    • The proposed algorithm achieves better symbol error rate (SER) performance.
    • Simulation results validate the effectiveness of BG-IOD over stochastic quadratic distance and dual mode constant modulus algorithms.
    • The BG soft-switching technique successfully balances SER convergence guarantee and performance enhancement.

    Conclusions:

    • The BG-IOD algorithm offers significant improvements for neural network-based nonlinear channel equalization.
    • Leveraging both input and output decision information enhances equalizer training and performance.
    • The proposed method provides a robust solution for challenging communication channel equalization tasks.