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Compared with multicolor-chip integrated white LEDs, phosphor-based white LEDs are more attractive for daily illumination due to lower cost and complexity, and thus they are preferable for future commercial use of visible light communication (VLC) systems. However, the application of phosphorescent white LEDs has a lower data rate than multicolor-chip integrated LEDs because of severe nonlinear impairments and limited bandwidth caused by the slow-responding phosphor. In this paper, for the first time we propose to employ phosphorescent white LEDs based on silicon substrate with adaptive bit-loading discrete multitone (DMT) modulation and a memoryless polynomial based nonlinear equalizer to achieve a high-speed VLC system. We also present a comprehensive comparison among nonlinear equalizers based on the Volterra series model, memory polynomial model, memoryless polynomial model and deep neural network (DNN) with experimental results utilizing a silicon substrate phosphorescent white LED, and provide detailed suggestions on how to choose the most suitable nonlinear mitigation scheme considering different practical conditions and the tradeoff between complexity and performance. Beyond 3.00 Gb/s DMT VLC transmission over 1-m indoor free space is successfully demonstrated with bit error rate (BER) under the 7% forward error correction (FEC) limit of 3.8×10−3. As far as we know, this is the highest data rate ever reported for VLC systems based on a single high-power phosphorescent white LED.

Visible light communication (VLC) is a promising research field in modern wireless communication. VLC has its irreplaceable strength including rich spectrum resources, no electromagnetic disturbance, and high-security guarantee. However, VLC systems suffer from the non-linear effects that exist in almost every part of the system. As a part of artificial intelligence, machine learning (ML) is showing its potential in non-linear mitigating for its natural ability to fit all kinds of transfer functions, which may dramatically push the research in VLC. This paper introduces the application of ML in VLC, describes five recent research of deep learning applications in VLC, and analyses the performance.

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