In this Letter, we propose a novel, to the best of our knowledge, neural network pre-equalizer based on the trial-and-error (TE) mechanism for visible light communication. This approach, unlike indirect learning (IL) architecture, does not require an additional auxiliary post-equalizer. Instead, it allows the pre-equalizer to be trained directly from the transmitter side through continuous interaction with the actual system. In a 1.95-Gbps 64-QAM carrier-less amplitude phase (CAP) free space optical transmission platform, the proposed scheme demonstrates superior nonlinear approximation capabilities and noise resilience. Specifically, the TE-recurrent neural network (RNN)-based pre-equalizer exhibits signal-to-noise ratio (SNR) gains of 0.8 dB and 1.8 dB over the IL-RNN-based and IL-Volterra-based pre-equalizers, respectively. We believe this is the first application of trial-and-error learning for training pre-equalizer in visible light communications.

中文翻译：

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具有试错学习功能的可见光通信预均衡方案

在这封信中，据我们所知，我们提出了一种新颖的基于可见光通信试错（TE）机制的神经网络预均衡器。与间接学习 (IL) 架构不同，这种方法不需要额外的辅助后均衡器。相反，它允许通过与实际系统的持续交互，直接从发射机侧训练预均衡器。在 1.95 Gbps 64-QAM 无载波幅度相位 (CAP) 自由空间光传输平台中，所提出的方案展示了卓越的非线性逼近能力和噪声恢复能力。具体而言，基于 TE 循环神经网络 (RNN) 的预均衡器的信噪比 (SNR) 增益比基于 IL-RNN 和 IL-Volterra 的预均衡器分别提高了 0.8 dB 和 1.8 dB，分别。我们相信这是试错学习在可见光通信中训练预均衡器的首次应用。