A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Wang, Jingjing; Zhang, Yingbo; Yan, Shefeng; Shi, Wei; Yang, Xiaobo; Guo, Ying; Gulliver, T. Aaron

doi:10.1109/tvt.2020.3044994

Cited by 77 publications

(35 citation statements)

References 40 publications

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“…σ 2 denotes the noise power of the underwater acoustic channel. Underwater acoustic channel noise is an important topic in the application practice of UACNs, as hydrostatic pressure effects (tides, waves, etc., caused by wind, rain, and seismic disturbances) and industrial behavior (e.g., surface sailing) remain one of the main reasons hindering the development of underwater acoustic communication [20][21][22]. Calculating the noise power σ 2 is a very complex challenge, because of the significant time-space-frequency variability of underwater acoustic channel noise [23,24].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Specifically, if the SINR in the current channel is greater than the predefined threshold η th (see ( 9)), i.e., the QoS is greater than the minimum requirement, the reward value is calculated from (21); otherwise, the reward value is 0. Overall, (22) is the payoff for choosing the power p i under state s t i to ensure the quality of service of the transmission, as well as to achieve energy efficiency.…”

Section: Reward Functionmentioning

confidence: 99%

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Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Yang

Wang

Fan

et al. 2022

Wireless Communications and Mobile Computing

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To solve the problems of poor quality of service and low energy efficiency of nodes in underwater multinode communication networks, a distributed power allocation algorithm based on reinforcement learning is proposed. The transmitter with reinforcement learning capability can select the power level autonomously to achieve the goal of getting higher user experience quality with lower power consumption. Firstly, we propose a distributed power optimization model based on the Markov decision process. Secondly, we further give a reward function suitable for multiobjective optimization. Finally, we present a distributed power allocation algorithm based on Q-learning and use it as an adaptive mechanism to enable each transmitter in the network to adjust the transmit power according to its own environment. The simulation results show that the proposed algorithm not only increases the total channel capacity of the system but also improves the energy efficiency of each transmitter.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Reward Functionmentioning

confidence: 99%

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Yang

Wang

Fan

et al. 2022

Wireless Communications and Mobile Computing

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“…The ability of the trained CNN as a feature extraction system is also explored by generating features and using them in conventional models. The proposed method can be extended to more challenging UWA channels, such as those with impulsive noise [13], by combining the CIR with impulsive noise for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

Lucas

Wang

2022

Applied Sciences

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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.

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“…[18,19]. It has more substantial applicability and great significance to describe the influence characteristics of underwater environmental noise interference [20] . In addition, in the current research on the sensing of underwater unknown frequency signals [21,20], there are problems that the sensing method can not fully estimate the unknown frequency signal parameters [22,23,24], and the signal-to-noise ratio threshold is too high [25,26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

A Practical Underwater Information Sensing System Based on Intermittent Chaos Under the Background of Lévy Noise

Zhang

Qin

Zhang

et al. 2022

Preprint

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The Gaussian noise model has been chosen for underwater information sensing tasks under substantial interference for most of the research at present. However, it often contains a strong impact and does not conform to the Gaussian distribution. In this paper, a practical underwater information sensing system is proposed based on intermittent chaos under the background of Lévy noise. In this system, a novel Lévy noise model is presented to describe the underwater natural environment interference and estimate its parameters, which can better describe the impact characteristics of the underwater environment. Then an underwater environment sensing method of dual-coupled intermittent chaotic Duffing oscillator is improved by using the variable step-size method and scale transformation. The simulation results show that the method can sense weak signals and estimate their frequencies under the background of strong Lévy noise, and the estimation error is as low as 0.03%. Compared with the intermittent chaos of the single Duffing oscillator and the intermittent chaotic Duffing of double coupling, the minimum SNR ratio threshold has been reduced by 11.5dB and 6.9dB, respectively, and the computational cost significantly reduced, and the sensing efficiency is significantly improved.

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A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Cited by 77 publications

References 40 publications

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses

A Practical Underwater Information Sensing System Based on Intermittent Chaos Under the Background of Lévy Noise

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