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. 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 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

A TESTBED DESIGN FOR MONITORING THE LONG-TERM SPATIAL-TEMPORAL DYNAMICS OF UNDERWATER ACOUSTIC CHANNELS

The underwater acoustic network testbed helps to validate the theoretical results and bridge the gap between experimental results. Characterizing and modeling the spatial-temporal dynamics of underwater acoustic channels is essential to developing efficient and effective physical-layer communication algorithms and network protocols. This work dedicates to designing a testbed system to measure the spatial-temporal dynamics of underwater acoustic channels. The collected measurements will shed insights into the spatial-temporal correlation of underwater acoustic channels and will be used to evaluate the theoretical algorithms that are designed to model the spatial-temporal dynamics and to exploit the spatial-temporal dynamics for more efficient and effective underwater system operations. The report speaks about how to tackle the above problem and discusses the following aspects in detail which are, individual node design which is controlled by a raspberry pi, comparison of the current test bed with the existing testbeds in field, complete description of the server algorithm and its error handling techniques, development of the server level GUI and web-based GUI and finally some of the experimentations carried out in Portage lake and Keweenaw bay