Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

Featured Application Convolutional neural networks are used on the channel impulse response data to predict the performance of underwater acoustic communications. Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one.

Automated summary

This article uses convolutional neural networks to learn useful features in underwater acoustic communication channels for predicting performance, outperforming traditional supervised learning models. The study also demonstrates the universality of the learned features across different channels.