A Variational Auto-Encoder Model for Underwater Acoustic Channels
Document Type
Conference Proceeding
Publication Date
11-22-2021
Department
Department of Electrical and Computer Engineering
Abstract
An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract representation of the UWA channel impulse responses (CIRs) and can generate CIR samples with similar features. A customized training process is proposed to avoid the model collapse and being trapped in a gradient pit. The proposed deep generative model is validated using field experimental data sets.
Publication Title
WUWNet 2021 - 15th ACM International Conference on Underwater Networks and Systems
ISBN
9781450395625
Recommended Citation
Wei, L.,
&
Wang, Z.
(2021).
A Variational Auto-Encoder Model for Underwater Acoustic Channels.
WUWNet 2021 - 15th ACM International Conference on Underwater Networks and Systems.
http://doi.org/10.1145/3491315.3491330
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16662