Blind Detection of Underwater Acoustic Communication Signals Based on Deep Learning

Li, Yongbin; Wang, Bin; Shao, Guofan; Shao, Shuai; Pei, Xilong

doi:10.1109/access.2020.3036883

Cited by 23 publications

(10 citation statements)

References 33 publications

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“…This omnidirectional property can be advantageous for underwater communication where the location of receiving nodes might be unknown or variable. Studies by [39] and [40] demonstrate the effectiveness of monopole antennas in underwater channels, achieving good radiation characteristics. However, their vertical radiation pattern can lead to signal energy being wasted by radiating upwards towards the water surface.…”

Section: Monopole Antennasmentioning

confidence: 99%

Antennas for Underwater Wireless Sensor Networks (UWSNs): Reviewing the Challenges of Underwater Communication

Paredes,

Arboleda

2024

Preprint

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Underwater communication for Underwater Wireless Sensor Networks (UWSNs) faces challenges due to the unique underwater environment. This review explores these challenges (attenuation, multipath propagation, limited bandwidth) across acoustic, optical, and radio frequency (RF) communication channels. It highlights how innovative antenna designs are crucial to mitigate these limitations and enable efficient data transmission. The review analyzes monopole, dipole, and helical antennas, discussing their trade-offs in radiation pattern, efficiency, and mitigating multipath effects. Advancements in materials, metamaterials, and reconfigurable antennas offer promising solutions for improved signal propagation, reduced attenuation, and better environmental adaptability. Research gaps include material exploration for low-conductivity antennas and optimizing antenna designs. Rigorous performance evaluation remains essential. Overall, advancements in underwater antenna technology are key to unlocking the full potential of UWSNs and enabling breakthroughs in marine applications.

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Section: Monopole Antennasmentioning

confidence: 99%

Antennas for Underwater Wireless Sensor Networks (UWSNs): Reviewing the Challenges of Underwater Communication

Paredes,

Arboleda

2024

Preprint

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“…Many researchers have integrated deep learning with signal recognition and processing, and empirical evidence has demonstrated the remarkable effectiveness of deep learning in the field of signal processing [4][5][6]. However, deep learning technologies impose high demands on experimental infrastructure, such as a high-quality graphics processing unit, making them inconvenient for use in certain specific scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm.

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“…Meanwhile, in LSTM, the mean absolute percentage error reached 3.14% [4]. Li et al [5] reported that the measurement of underwater communication in a non-test field is challenging owing to the complex underwater channels, the variety of transmission signals, and the amount of measurement data. Thus, their team used the generative adversarial network to mitigate noise in signals and employed a convolutional neural network to distinguish noise from the true signal captured in an underwater communication environment.…”

Section: Introductionmentioning

confidence: 99%

“…The model was used to generate data from other environments to overcome the problem of insufficient data for the target waters [5]. To improve the performance of traditional systems concerning a low noise ratio and multipath effects, Liu et al [6] proposed a deep-learning-based cyclic shift keying spread spectrum (CSK-SS) UWA communication system and demodulated the received signals by using neural network models constructed using LSTM and bidirectional LSTM (BiLSTM).…”

Section: Introductionmentioning

confidence: 99%

Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

Chih-Ta Yen,

Un-Hung Chen

2024

Int. j. eng. technol. innov.

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In this study, deep learning network technology is employed to solve the problem of rapid changes in underwater channels. The modulation techniques employed are frequency-shift keying (FSK) and the BELLHOP module of MATLAB; they are used to create water with multipath, Doppler shifts, and additive Gaussian white noise such that underwater acoustic receiving signals simulating the actual ocean environment can be obtained. The southwest coastal area of Taiwan is simulated in the manuscript. The results reveal that optimizing the environment by using the virtual time reversal mirror (VTRM) technique can generally mitigate the bit error rates (BERs) of the deep learning network’s model receiver and traditional demodulation receiver. Lastly, seven deep learning networks are deployed to demodulate the FSK signals, and these approaches are compared with traditional demodulation techniques to determine the deep learning network techniques that are most suitable for marine environments.

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Blind Detection of Underwater Acoustic Communication Signals Based on Deep Learning

Cited by 23 publications

References 33 publications

Antennas for Underwater Wireless Sensor Networks (UWSNs): Reviewing the Challenges of Underwater Communication

Antennas for Underwater Wireless Sensor Networks (UWSNs): Reviewing the Challenges of Underwater Communication

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

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