Ai-enabled efficient modulation classification in underwater OWC systems

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.