Data-Driven Signal Detection for Underwater Acoustic Filter Bank Multicarrier Communications

Zhu, Yunan; Wang, Biao; Xie, Fangtong; Wu, Chengxi; Peng, Chao

doi:10.1155/2022/4943442

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…In this sense, recent advancements have leveraged sophisticated signal-processing techniques and machine learning to enhance communication capabilities in these challenging environments. For example, the work described in [6] employed a neural network with feature extraction and automatic learning ability to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. A UWA communication modulation classifier is proposed in [7], which uses a convolutional neural network (CNN) to identify the presence of the UWA signals; then, the classifier uses the feature vector output by the encoder to distinguish the final modulation categories.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an SVM-Powered Underwater Acoustic Modem

Guerrero-Chilabert,

Moreno-Salinas,

Sánchez-Moreno

2024

JMSE

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Underwater acoustic communication is fraught with challenges, including signal distortion, noise, and interferences unique to aquatic environments. This study aimed to advance the field by developing a novel underwater modem system that utilizes machine learning for signal classification, enhancing the reliability and clarity of underwater transmissions. This research introduced a system architecture incorporating a Lattice Semiconductors FPGA for signal modulation and a half-pipe waveguide to emulate the underwater environment. For signal classification, support vector machines (SVMs) were leveraged with the continuous wavelet transform (CWT) employed for feature extraction from acoustic signals. Comparative analysis with traditional signal processing techniques highlighted the efficacy of the CWT in this context. The experiments and tests carried out with the system demonstrated superior performance in classifying modulated signals under simulated underwater conditions, with the SVM providing a robust classification despite the presence of noise. The use of the CWT for feature extraction significantly enhanced the model’s accuracy, eliminating the need for further dimensionality reduction. Therefore, the integration of machine learning with advanced signal processing techniques presents a promising research line for overcoming the complexities of underwater acoustic communication. The findings underscore the potential of data mining methodologies to improve signal clarity and transmission reliability in aquatic environments.

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Section: Introductionmentioning

confidence: 99%

Design and Development of an SVM-Powered Underwater Acoustic Modem

Guerrero-Chilabert,

Moreno-Salinas,

Sánchez-Moreno

2024

JMSE

View full text Add to dashboard Cite

show abstract

Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems

Rahman

Sejan

Aziz

et al. 2023

IEEE Trans. on Green Commun. Netw.

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Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System

Rahman

Sejan

Yoo

et al. 2022

Sensors

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Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond.

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Data-Driven Signal Detection for Underwater Acoustic Filter Bank Multicarrier Communications

Cited by 3 publications

References 29 publications

Design and Development of an SVM-Powered Underwater Acoustic Modem

Design and Development of an SVM-Powered Underwater Acoustic Modem

Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems

Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System

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