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Low spatial complexity adaptive artificial neural network post-equalization algorithms in MIMO visible light

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    We developed a low-complexity adaptive artificial neural network (AANN) for multiple-input multiple-output (MIMO) visible light communication (VLC) systems. This AANN post-equalization algorithm achieves high data rates with reduced spatial complexity and maintained bit error rate performance.

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    Area of Science:

    • Optical Communications
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Visible Light Communication (VLC) systems offer high bandwidth potential.
    • Multiple-Input Multiple-Output (MIMO) techniques enhance VLC capacity.
    • Post-equalization is crucial for mitigating signal distortion in high-speed VLC.

    Purpose of the Study:

    • To propose a feasible and low spatial complexity adaptive artificial neural network (AANN) post-equalization algorithm for MIMO VLC systems.
    • To reduce the spatial complexity of artificial neural network (ANN) post-equalization algorithms.
    • To maintain or improve the bit error rate (BER) performance while reducing complexity.

    Main Methods:

    • Introducing power ratio and MIMO least mean square (MIMO-LMS) into ANN design.
    • Developing an adaptive artificial neural network (AANN) post-equalization algorithm.
    • Implementing the algorithm in a single-receiver MIMO (SR-MIMO) VLC system using 16QAM SCM and CAP modulation.

    Main Results:

    • Reduced spatial complexity of the post-ANN equalization algorithm to less than 10%.
    • Maintained the bit error rate (BER) performance of adaptive artificial neural networks (AANNs).
    • Achieved a data rate of 2.1 Gbps in the experimental setup.

    Conclusions:

    • The proposed AANN post-equalization algorithm is effective for MIMO VLC systems.
    • Low spatial complexity and high data rates are achievable simultaneously.
    • This approach offers a practical solution for advanced VLC system design.