A Variational Auto-Encoder Model for Underwater Acoustic Channels

被引：1

作者：

Wei, Li ^{[1
]}

Wang, Zhaohui ^{[1
]}

机构：

[1] Michigan Technol Univ, Houghton, MI 49931 USA

来源：

WUWNET'21: THE 15TH ACM INTERNATIONAL CONFERENCE ON UNDERWATER NETWORKS & SYSTEMS | 2021年

关键词：

Generative model; variational autoencoder; underwater acoustic communication;

D O I：

10.1145/3491315.3491330

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：5

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