Transport vs. Deep Neural Networks in OAM Underwater Communications

Signal attenuation, resulting in low signal to noise ratio (SNR), in underwater optical communications (UWOC) is a problem that degrades classification performance. We develop and contrast several novel ways to create machine learning (ML) and optimal transport-based attenuation models and insert these models in a convolutional neural network (CNN) classification training pipeline. We show that including these ML-based attenuation models in the CNN classifier training, significantly improves classification performance.