Experimental evaluation of the impact of physical beam misalignment on the performance of an underwater wireless optical communication network utilizing machine learning

Underwater wireless optical communication systems have garnered significant interest due to advantages in speed and security over radio-frequency and acoustic underwater transmission. One example utilizes Laguerre- Gaussian beams carrying orbital angular momentum to increase bit transfer rate by spatially multiplexing information into an alphabet of symbols, sized 2N, where N is the number of bits encoded in each symbol. This system uses a convolutional neural network (CNN) to demultiplex the information, which has been shown to be capable of high levels of accuracy even in turbid environments or in the presence of significant optical turbulence. However, it has been observed that CNNs struggle when shown images affected by physical environmental effects (different levels of turbulence, optical magnification, beam misalignment) that were not present in the training set. In this paper we further investigate physical beam misalignment under two experimental conditions, quiescent and strong optical turbulence. Physical beam misalignment is defined as a translation of the beam at the receiver. Our optically turbulent environment is characterized using the scintillation of a Gaussian beam, and is estimated to have a refractive index structure constant value of similar to 10-11 m-2/3. We use two alphabet sizes, of 16 and 256 symbols, and demonstrate classification of received images in a combination of scenarios with adequate success (>90% accuracy) when training and testing under the same conditions, and using a pre-trained CNN, AlexNet. However, when training and testing under physically misaligned conditions, we demonstrate an issue with impractical rates of classification, at 5%-50%. Our investigation provides CNN classification at significantly higher levels of complexity than previously seen, both in terms of experimental turbulence strength and number of classifiable symbols (alphabet size), and clearly demonstrates the importance of carefully designed experiments to aid the machine learning training process for communication systems.