In this paper, we propose and experimentally demonstrate an artificial intelligence (AI)-enabled efficient modulation classification technique for underwater optical wireless communication (UOWC) systems. Specifically, time-domain waveform histograms are adopted as classification features, where three modulation formats including direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM) and pulse amplitude modulation (PAM) are considered. Moreover, AI algorithms such as decision trees (DT), k-nearest neighbors (k-NN), support vector machines (SVM) and convolutional neural networks (CNN) are utilized to realize efficient modulation classification based on the obtained waveform histogram features. Experimental results demonstrate that all the four algorithms can achieve accuracy surpassing 95% when the received signal-to-noise ratio (SNR) exceeds 6.3 dB. Furthermore, increasing the number of symbols in histograms enhances classification accuracy, whereas altering the number of histogram bins has minimal impact on classification accuracy.

在本文中，我们提出并实验演示了一种基于人工智能 （AI） 的水下光无线通信 （UOWC） 系统高效调制分类技术。具体来说，采用时域波形直方图作为分类特征，其中考虑了三种调制格式，包括直流偏置光正交频分复用 （DCO-OFDM）、非对称削波光 OFDM （ACO-OFDM） 和脉冲幅度调制 （PAM）。此外，利用决策树 （DT）、k 最近邻 （k-NN） 、支持向量机 （SVM） 和卷积神经网络 （CNN） 等人工智能算法，基于获得的波形直方图特征实现高效的调制分类。实验结果表明，当接收信噪比 （SNR） 超过 6.3 dB 时，四种算法的准确率均能达到 95% 以上的精度。此外，增加直方图中的符号数量可以提高分类精度，而更改直方图条柱的数量对分类精度的影响最小。