Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

Applied Sciences

Zhaohui Wang¹  Evan Lucas¹ 

[1] Department of Electrical and Computer Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA;

关键词: underwater acoustic communications;    regression;    convolutional neural networks;    deep learning;    channel impulse response;   

DOI  :  10.3390/app12031086

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown