Document Type
Article
Publication Date
1-20-2022
Department
Department of Electrical and Computer Engineering
Abstract
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.
Publication Title
Applied Sciences (Switzerland)
Recommended Citation
Lucas, E.,
&
Wang, Z.
(2022).
Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses.
Applied Sciences (Switzerland),
12(3).
http://doi.org/10.3390/app12031086
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15668
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/app12031086