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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Deep-learning-based multi-user framework for end-to-end fiber-MMW communications.

Zhongya Li, Junlian Jia, Guoqiang Li

    Optics Express
    |May 9, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a deep learning framework for fiber-integrated wireless communication, enhancing multi-user capacity and receiver sensitivity in millimeter-wave systems.

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    Area of Science:

    • Optical Communications
    • Wireless Communication
    • Artificial Intelligence

    Background:

    • Fiber-wireless integration is crucial for sixth-generation (6G) wireless networks.
    • Artificial intelligence (AI) is increasingly empowering advanced communication systems.

    Purpose of the Study:

    • To propose and demonstrate a deep learning-based end-to-end (E2E) multi-user communication framework for fiber-millimeter wave (MMW) systems.
    • To jointly optimize transmission for multiple users within a single fiber-MMW channel.

    Main Methods:

    • Developed an E2E framework utilizing artificial neural networks (ANNs) as transmitters, channel models (ACM), and receivers.
    • Employed a two-step transfer learning technique to train the ANN-based channel models for fiber-MMW channel matching.
    • Connected computation graphs of multiple transmitters and receivers for joint optimization.

    Main Results:

    • Achieved over 3.5 dB receiver sensitivity gain in a single-user scenario compared to single-carrier QAM.
    • Demonstrated a 1.5 dB receiver sensitivity gain in a three-user scenario under the 7% hard-decision forward error correction threshold.
    • Successfully transmitted at 46.2 Gbit/s over a 10-km fiber-MMW link.

    Conclusions:

    • The proposed E2E deep learning framework effectively supports multi-user access in fiber-MMW integrated systems.
    • The framework offers significant receiver sensitivity improvements, crucial for future high-capacity wireless networks.
    • AI-driven optimization in integrated systems represents a promising direction for 6G advancements.