Modulation Identification of Underwater Acoustic Communications Signals Based on Generative Adversarial Networks

Yao, Xiaohui; Yang, Honghui; Li, Yiqing

doi:10.1109/oceanse.2019.8867125

Cited by 25 publications

(11 citation statements)

References 5 publications

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“…Lastly, DBN based classification of the recreated signals occurs. Jiang et al [10] proposed the PCA for effective extraction of power spectral and square spectral feature of UWA signal at the existence of noise, multipath, and Doppler made in UWA channel. With the feature attained by PCA, an ANN classification is adapted for recognizing modulation of UWA transmission signal.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yao et al [10] introduced a modulation recognition technique based generative adversarial network (GAN) for increasing the strength of modulation recognition for UWA transmission signals. The generator of GAN has trained for improving the distorted signal and the discriminator is trained for extracting feature in UWA transmission signal and automatically categorizing them.…”

Section: Literature Reviewmentioning

confidence: 99%

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Ensemble of Deep Learning Enabled Modulation Signal

Indumathi²,

Vijay

2022

Preprint

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Automated classification of underwater acoustic signals allows to effectively utilize the acoustic spectrum by certain actions such as interference circumvention and marine mammal protections. The commonly utilized modulation techniques for underwater acoustic communication are MPSK (BPSK, QPSK, and 8PSK) and MQAM. Detection of modulation type of a received waveform enables sonar and communication in a similar bandwidth with least collision and it can determine the system functioning exterior to the permitted regime. Automated modulation classification models can make utilize of machine learning (ML) and deep learning (DL) techniques, particularly for underwater acoustic communication. With this motivation, this study designs a new ensemble of deep learning based modulation signal classification (EDL-MSC) models for underwater acoustic communication. Initially, an impulsive noise pre-processor was used for eliminating the impulse from the target signal. Besides, three deep learning models namely bi-directional long short term memory (BiLSTM), gated recurrent unit (GRU), and stacked sparse autoencoder (SSAE) models are used to derive features in the temporal waveform and square spectra of the pre-processed signal. In addition, black widow optimization (BWO) is applied for the optimal hyperparameter tuning of the DL models. Lastly, an ensemble of voting schemes is applied to integrate the outcome of the three DL models. The proposed model has the ability to perform effective modulation classification process in underwater acoustic communication. The performance validation of the EDLMSC technique takes place under several aspects and the comprehensive comparative analysis ensured the supremacy of the EDL-MSC technique over the recent approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Ensemble of Deep Learning Enabled Modulation Signal

Indumathi²,

Vijay

2022

Preprint

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“…In addition, marine acoustic observation technology started late, and underwater acoustic signals have complex characteristics such as sparsity, diversity, and nonstationarity, and there is a lack of standardized large-scale underwater acoustic signal data sets. Therefore, the research on underwater targets using underwater acoustic observation mainly focuses on the problem of how to reduce the effect of background noise and extract the target signal from nonstationary nonlinear ocean acoustic observation signals submerged in noise in the absence of prior knowledge of the signal [5].…”

Section: Introductionmentioning

confidence: 99%

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

et al. 2023

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For the acquisition of ocean observation information, the underwater acoustic signal is the only known medium that can propagate over long distances in water. However, the underwater acoustic field environment is highly time-varying and spatially variable, and ocean acoustic observation signals are mixed Gaussian, non-cooperative, and nonlinear. Therefore, feature extraction of underwater target signals has been a challenging research direction. The high-order domain feature extraction method of ocean acoustic observation signal is widely used because of its advantages of anti-Gaussian background noise and preservation of signal phase information. Firstly, this paper analyzes three aspects, including the characteristics of ocean acoustic observation signals, the limitations of second-order domain processing methods, and advantages of high-order domain processing methods. Then, this paper focuses on theoretical advantages and application areas of signal high-order domain feature extraction technology. The technical development and application performance of common feature extraction technology in the field of ocean acoustic observation signal processing by higher-order domain methods in recent years are analyzed in the paper, and the challenges and future trends of higher-order domain feature extraction technology in marine acoustic observation signal processing are discussed in the context of recent studies.

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“…Moreover, an evaluation is presented in terms of classification categories, robustness against environments, and application limitations. [16] Requires an accurate estimation about signal carrier frequency CNN + LSTM [17] Signal waveforms on carrier CNN [18] The classification categories are limited; DAE + SAE [19] Not robust against the impulsive noise environments Instantaneous features of signal waveforms LSTM [20] Requires a large amount of training data from the testing channel Spectral sequence or diagrams of signals SAE [21] Requires a sufficient number of transmitted symbols; performance deteriorates sharply when the channel fading is poor CNN [22] Signal spectrograms CNN [23] Requires a trade-off between the time and frequency resolutions; Unable to perform inter-class recognition for PSK signals…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, their performance deteriorates sharply when the channel fading is poor. Yao et al [23] designed a deeper and wider CNN, which takes the spectrograms of the signals as input. This method is more robust against multi-path channels but leads to a trade-off between the time and frequency resolutions.…”

Section: Introductionmentioning

confidence: 99%