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Performance Prediction Of Underwater Acoustic Communications Based On Channel Impulse Responses, Evan Lucas, Zhaohui Wang

Michigan Tech Publications

Featured Application: Convolutional neural networks are used on the channel impulse response data to predict the performance of underwater acoustic communications. Abstract: 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 …