Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

Lucas, Evan; Wang, Zhaohui

doi:10.3390/app12031086

Cited by 9 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [71], a one-dimensional CNN was employed for predicting the performance of UWA communications based on CIR. Two datasets were used in the experiments, and the results demonstrated that the predictive performance of the CNN was superior to traditional machine learning methods on any of the datasets.…”

Section: Algorithm 7 Lstm Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.

show abstract

Section: Algorithm 7 Lstm Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using a computation template, create characteristic maps from each target region that are the same size. After that, get a Support Vector Machine (SVM) [11] and find all the potential regions' category entries. For every screen that qualifies, this R-CNN approach uses algebraic image distortion to determine the inputs of a completely Convolutional with a constant size, regardless of its appearance.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

View full text Add to dashboard Cite

“…Channel modeling is important for the estimation of transmission quality. In [11] and [12], the authors present methods of underwater channel modeling. The channel impulse response can also be utilized for the estimation of underwater platform motion parameters [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Impulse Responses Measured in Motion in a Towing Tank

et al. 2022

View full text Add to dashboard Cite

The growing interest in developing autonomous underwater vehicles (AUVs) and creating underwater sensor networks (USNs) has led to a need for communication tools in underwater environments. For obvious reasons, wireless means of communication are the most desirable. However, conducting research in real conditions is troublesome and costly. Moreover, as hydroacoustic propagation conditions change very significantly, even during the day, the assessment of proposed underwater wireless communication methods is very difficult. Therefore, in the literature, there are considered simulators based on real measurements of underwater acoustic (UWA) channels. However, these simulators make an assumption that, during the transmission of elementary signals, the impulse response does not change. In this article, the authors present the results of the measurements realized in a towing tank where the transmitter could move with a precisely set velocity and show that the analyzed channel was non-stationary, even during the time of the transmission of a single chirp signal. The article presents an evaluation method of channel stationarity at the time of the chirp transmission, which should be treated as novelty. There is also an analysis of the impulse responses measured in motion in a towing tank.

show abstract

Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

Cited by 9 publications

References 19 publications

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Analysis of Impulse Responses Measured in Motion in a Towing Tank

Contact Info

Product

Resources

About