Decreasing noise in acoustic underwater transmissions using deep neural networks

The accuracy and reliability of ocean communications and transmissions are affected by many sources of offshore distortion and background noise.  Decreasing this interference in ocean transmissions is dependent on the process of channel estimation, or the assessment of background channel characteristics that may distort or interfere with the received signal.  Recent modeling has enhanced channel estimation performance using deep neural networks and improved the denoising of ocean transmissions.

 

Scientists from Xiamen University and the University of Witwatersrand designed a new model using machine learning to better characterize underwater acoustic channel (UAC) interference and improve the accuracy of ocean and offshore transmissions.  Importantly, the deep learning model facilitates the detection of sparse, or rare, interference in channels despite the presence of other, more common interference such as ambient noise, which was not possible with traditional denoising models.  In doing so, the researchers more accurately modeled channel interference and improved channel estimation performance, increasing the precision of underwater transmissions in an exceptionally challenging environment.  

 

The team published their findings in the March, 30 issue of Intelligent and Converged Networks, published by Tsinghua University Press.  

 

“Effectively obtaining the sparse features of the underwater acoustic channels is very important for … channel estimation performance and thus the transmission reliability of the system,” said Sicong Liu, first author of the research study and associate professor in the School of Informatics at Xiamen University.  “This paper proposed a model-driven sparse learning-based approach with denoising capability, which is a meaningful effort towards reliable underwater transmission in harsh ocean environments,” said Liu.  Improving underwater transmissions will not only enhance underwater communication, but also the accuracy of oceanic science and monitoring and the efficiency of marine resource acquisition.

 

The research team’s new denoising model does a better job of estimating less common forms of interference that occur in marine environments by leveraging machine learning.  “The proposed method is incorporating a denoiser in … model-driven, sparsity-aware deep neural networks, which makes it possible to learn the sparse features of the underwater acoustic channel in the presence of intensive noise. Compared with traditional non-learning-based, anti-interference or anti-noise methods, the proposed methods have better performance of channel estimation,” said Liu.  Importantly, transceiver or receiver movement and mobility and ambient noise are only some of the factors that may contribute to the interference experienced in underwater environments.

 

Liu and his team are looking for additional ways to decrease channel noise through improved channel estimation.  In the shorter term, newer transformer machine learning models could improve channel estimation by programming self-attention functions capable of independently considering the importance of incoming data.  This function may be particularly useful for channels plagued by time variance in transmitted or received signals, which is a common occurrence in underwater environments.  Signals transmitted from a single source can take many pathways before reaching a particular receiver, bouncing off of the surface of the water or the bottom of the ocean, changing the path, signal strength and amount of time required to reach a receiver.  Additional marine acoustic models could also be considered with additional data and environmental testing.

 

Ultimately, the study authors envision a system designed to incorporate machine learning on both sides of transmissions to most accurately solve the channel interference problem.  “We might want to design an AI-enabled, end-to-end underwater acoustic transmission system, which not only deals with channel estimation using learning approaches like the model outlined … in this paper, but also solves the entire transmission task based on emerging AI techniques,” said Liu.  Ideally, machine learning models, if properly designed, will be able to distinguish signal from noise independently and denoise transmissions through autonomous, accurate channel estimation.

 

Other contributors include Younan Mou, Xianyao Wang and Danping Su from the School of Informatics at Xiamen University in Xiamen, China and Ling Cheng from the Information Engineering at the University of the Witwatersrand in Johannesburg, South Africa.

 

This work was supported by the National Natural Science Foundation of China (No. 61901403), the Science and Technology Key Project of Fujian Province, China (Nos. 2021HZ021004 and 2019HZ020009), the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2023D10), the Youth Innovation Fund of Natural Science Foundation of Xiamen (3502Z20206039), the Science and Technology Key Project of Xiamen (No. 3502Z20221027) and the Xiamen Special Fune for Marine and Fishery Development (No. 21CZB011HJ02).

 

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About Intelligent and Converged Networks 

 

Intelligent and Converged Networks is an international specialized journal that focuses on the latest developments in communication technology. The journal is co-published by Tsinghua University Press and the International Telecommunication Union (ITU), the United Nations specialized agency for information and communication technology (ICT). Intelligent and Converged Networks draws its name from the accelerating convergence of different fields of communication technology and the growing influence of artificial intelligence and machine learning.

 

About Tsinghua University Press

 

Established in 1980, belonging to Tsinghua University, Tsinghua University Press (TUP) is a leading comprehensive higher education and professional publisher in China. Committed to building a top-level global cultural brand, after 41 years of development, TUP has established an outstanding managerial system and enterprise structure, and delivered multimedia and multi-dimensional publications covering books, audio, video, electronic products, journals and digital publications. In addition, TUP actively carries out its strategic transformation from educational publishing to content development and service for teaching & learning and was named First-class National Publisher for achieving remarkable results.

 

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