论文标题
水下声学通信渠道使用深度学习
Underwater Acoustic Communication Channel Modeling using Deep Learning
论文作者
论文摘要
随着水下活动数量的最新增加，具有有效的水下通信系统变得越来越重要。水下声学通信已被广泛使用，但由于水下环境的复杂性而受到了极大的损害。为了更好地了解水下声学通道，以帮助水下通信系统的设计和改进，已经尝试使用数学方程和一些假设下的数学方程和近似值来对水下声学通道进行建模。在本文中，我们探讨了机器学习和深度学习方法的能力，可以使用从有干扰和塔霍湖的水箱中收集的实际水下数据来学习和准确对水下声学通道进行建模。具体而言，深度神经网络（DNN）和长期短期记忆（LSTM）用于对水下声学通道进行建模。实验结果表明，这些模型能够很好地对水下声学通信通道进行建模，并且在平均绝对百分比误差方面，深度学习模型（尤其是LSTM）是更好的模型。
With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important. Underwater acoustic communication has been widely used but greatly impaired due to the complicated nature of the underwater environment. In a bid to better understand the underwater acoustic channel so as to help in the design and improvement of underwater communication systems, attempts have been made to model the underwater acoustic channel using mathematical equations and approximations under some assumptions. In this paper, we explore the capability of machine learning and deep learning methods to learn and accurately model the underwater acoustic channel using real underwater data collected from a water tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network (DNN) and Long Short Term Memory (LSTM) are applied to model the underwater acoustic channel. Experimental results show that these models are able to model the underwater acoustic communication channel well and that deep learning models, especially LSTM are better models in terms of mean absolute percentage error.
