Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel

Zhang, Youwen; Li, Junxuan; Zakharov, Yuriy; Li, Jianghui; Li, Yingsong; Lin, Chuan; Li, Xiang

doi:10.1109/access.2019.2906424

Cited by 46 publications

(14 citation statements)

References 28 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deep neural network (DNN)-based OFDM receiver proposed in [13,14] was shown to be more robust than conventional methods; this model was extended to deal with the time-varying underwater acoustic channel and was verified by simulations [7]. A DNN-based online-training UWA receiver [15] was trained by the adaptive moment estimation (Adam) optimizer for each sub-block for the time-varying UWA channel and was verified by sea trial data. To reduce the training burden, model-driven deep learning [16] used expert knowledge to make the network explainable and predictable.…”

Section: Introductionmentioning

confidence: 92%

Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications

Yao

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

This study proposes a novel receiver structure for underwater vertical acoustic communication in which the bias in the correlation-based estimation for the timing offset is learned and then estimated by a deep neural network (DNN) to an accuracy that renders subsequent use of equalizers unnecessary. For a duration of 7 s, 15 timing offsets of the linear frequency modulation (LFM) signals obtained by the correlation were fed into the DNN. The model was based on the Pierson–Moskowitz (PM) random surface height model with a moderate wind speed and was further verified under various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. The 5000 m depth deep-sea experimental data show the superiority of the proposed combination of DNN-based synchronization and the time-invariant equalizer.

show abstract

Section: Introductionmentioning

confidence: 92%

Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications

Yao

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Youwen Zhang, 𝑒𝑡 𝑎𝑙 . used the deep neural network (DNN) to build a deep learning based receiver in [28] for single carrier communication in an underwater acoustic channel using data gotten from the sea. The deep neural network (DNN) based receiver consistently performed better in different simulation configurations using features extracted by the deep network compared to the traditional channel-estimate (CE)-based decision feedback equalizer (DFE).…”

Section: Related Researchmentioning

confidence: 99%

Underwater Acoustic Communication Channel Modeling using Deep Learning

Onasami¹,

Adesina²,

Qian³

2022

Preprint

View full text Add to dashboard Cite

With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important. Underwater acoustic communication has been widely used but greatly impaired due to the complicated nature of the underwater environment. In a bid to better understand the underwater acoustic channel so as to help in the design and improvement of underwater communication systems, attempts have been made to model the underwater acoustic channel using mathematical equations and approximations under some assumptions. In this paper, we explore the capability of machine learning and deep learning methods to learn and accurately model the underwater acoustic channel using real underwater data collected from a water tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network (DNN) and Long Short Term Memory (LSTM) are applied to model the underwater acoustic channel. Experimental results show that these models are able to model the underwater acoustic communication channel well and that deep learning models, especially LSTM are better models in terms of mean absolute percentage error.

show abstract

“…In traditional wireless communications, processing blocks such as modulation, channel estimation and channel equalization rely on the mathematically expressed models. However, in some practical scenarios, the interaction between the signal and the environment becomes extremely complex and establishing such mathematical models are intractable [26], [27]. Combining the deep learning with the mature existed mathematically expressed model or associated algorithm for processing block can boost the intelligent communication and achieve comparable performance gains [28].…”

Section: System Designmentioning

confidence: 99%

Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

et al. 2020

View full text Add to dashboard Cite

In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. To address this issue, a novel deep learning aided signal detection scheme is proposed in this paper. By exploiting the physical mechanism and prior expert knowledge of the signal processing, a two-connected multilayer perception (MLP) network is integrated into the receiver. The first subnetwork is regarded as a channel compensation block while the second one works as a demodulator. With sophisticated numerical optical channel model and SPAD non-Poisson model, large amounts of training data are utilized to train the proposed model offline. Afterwards, the online data are recovered with the trained network. Simulation results verify that significant bit error ratio (BER) improvement can be achieved with the proposed scheme. INDEX TERMS Underwater optical wireless communication, nonlinear distortion, deep learning, multilayer perception, signal detection. I. INTRODUCTION With the application of technologies such as massive multi-input multi-output transmission, millimeter-wave (mm-mave) communication and non-orthogonal multiple access scheme, 5G mobile communication has significantly increased the system capacity and supported massive connections [1]. However, the 5G network is still ground based [2]. Vast communication demand at sea is greatly challenged due to the limited 5G network coverage. Thus, the envisaged 6G network is expected to provide global wireless connectivity from space to underwater. As a complementary technology for terrestrial communication, optical wireless communication, like laser communication or visible light The associate editor coordinating the review of this manuscript and approving it for publication was Zinan Wang .

show abstract

Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel

Cited by 46 publications

References 28 publications

Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications

Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications

Underwater Acoustic Communication Channel Modeling using Deep Learning

Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

Contact Info

Product

Resources

About