Modulation Classification of Underwater Communication with Deep Learning Network

Wang, Yan; Zhang, Hao; Sang, Zhanliang; Xu, Lingwei; Cao, Conghui; Gulliver, T. Aaron

doi:10.1155/2019/8039632

Cited by 34 publications

(17 citation statements)

References 27 publications

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“…This has been achieved more broadly for communication networks using a variety of processes [12], [13]. In addition, researchers have previously applied machine learning technology to underwater networks in order to overcome poor link quality [14]- [18].…”

Section: Previous Studiesmentioning

confidence: 99%

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Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Byun

Cho

et al. 2021

IEEE Access

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Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

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Section: Previous Studiesmentioning

confidence: 99%

“…Multiple kinds of classification models are used to predict MCS in [14], [22], [29], [30]. The underwater environment dataset is trained and extracted the feature, using the CNN classification model in [14]. In addition, [22], [30] predicted MCS values by using boosted tree; [30] shows 99% accuracy.…”

Section: Previous Studiesmentioning

confidence: 99%

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Byun

Cho

et al. 2021

IEEE Access

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“…There are some new methods of underwater acoustic communication such as sparse adaptive convolution cores, time-domain turbo equalization, and frequency-domain turbo equalization have been used, but these methods still have the problem of high computational complexity and low classification success rate. By applying Deep Learning, classification accuracy has found around 99% [15].…”

Section: Introductionmentioning

confidence: 99%

Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach

2020

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Interest in the study of next-generation underwater sensor networks for ocean investigations has increased owing to developing concerns over their utilization in areas such as oceanography, commercial operations in maritime areas, and military surveillance. Underwater acoustic communications (UAC) network channels are fast-varying (spatially and temporally) according to environmental conditions. It is tempting to use adaptive modulation and coding (AMC) for UAC networks to improve the system efficiency by matching transmission parameters to channel variations. This paper focuses on analyzing a measured sea trial dataset by using a rule-based strategy (i.e., three-dimensional analysis, modulation-wise analysis, and a fixed-SNR strategy) to find the suitable link adaptation procedure depending on the channel quality. Hence, we plot a scenario of the measured UAC network data rate vs. Signal to Noise Ratio (SNR) and/or Bit Error Rate (BER) to pick the best AMC combinations in the context of adaptivity to the channel. Due to non-reversibility limitation of rule-based strategy, the work further extends to use machine learning (ML) algorithms to classify the MCS levels by investigating the channel characteristics. Boosted regression tree, from among the four ML algorithms we adopted for the analysis, shows formidable accuracy of 99.97% in classifying MCS levels. This ensemble of trees learns from the uplink data of the buoy and the base station and relates the MCS levels to channel metrics and signal characteristics especially subject to SNR and BER constraints. INDEX TERMS Boosted regression tree analysis, K-nearest neighbors, link adaptation, machine learning, pseudo-linear discriminant analysis, support vector machine, UAC network.

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“…e signal analysis and processing can be carried out only when the modulation of the signal is recognized [3]. It finds applications in various commercial and military areas.…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Sun

et al. 2020

Mathematical Problems in Engineering

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With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model’s ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal.

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Modulation Classification of Underwater Communication with Deep Learning Network

Cited by 34 publications

References 27 publications

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Link Adaptation on an Underwater Communications Network Using Machine Learning Algorithms: Boosted Regression Tree Approach

Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

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