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Reinforcement learning-based adaptive beam alignment in a photodiode-integrated array antenna module.

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    PubMed
    Summary
    This summary is machine-generated.

    We developed an intelligent adaptive beam alignment system using reinforcement learning (RL) for millimeter wave (MMW) communications. This system optimizes antenna phase for superior signal quality, meeting 3GPP standards.

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    Area of Science:

    • Wireless Communications
    • Antenna Technology
    • Artificial Intelligence

    Background:

    • Millimeter wave (MMW) communication systems require precise beam alignment for optimal performance.
    • Traditional beam alignment methods can be complex and time-consuming.
    • Adaptive antenna systems are crucial for dynamic wireless environments.

    Purpose of the Study:

    • To demonstrate an intelligent adaptive beam alignment scheme using reinforcement learning (RL).
    • To integrate RL with an 8x8 photonic array antenna for MMW applications.
    • To autonomously optimize beam alignment for enhanced signal quality.

    Main Methods:

    • Utilized a reinforcement learning (RL) algorithm with Q-table for decision-making.
    • Represented RL states as phase values, actions as phase changes, and rewards as error vector magnitude (EVM).
    • Integrated the RL scheme with a 40 GHz photonic array antenna.

    Main Results:

    • The RL scheme autonomously achieved optimal phase for the best EVM performance.
    • Successfully demonstrated beam alignment in both single- and multiple-user scenarios.
    • Experimental results consistently met the 3rd Generation Partnership Project (3GPP) criterion for 64-QAM OFDM.

    Conclusions:

    • The proposed intelligent adaptive beam alignment scheme effectively optimizes MMW communication.
    • Reinforcement learning provides an autonomous and efficient method for beam alignment.
    • The system ensures reliable signal quality meeting stringent industry standards.