Influence of Temporal and Spatial Fluctuations of the Shallow Sea Acoustic Field on Underwater Acoustic Communication

Lv, Zhichao; Du, Liang; Li, Huming; Wang, Lei; Qin, Jixing; Yang, Min; Ren, Chao

doi:10.3390/s22155795

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…In the following simulations, a single-carrier SISO system with quadrature phase shift keying (QPSK) modulation is studied. The source data are thrown into a 1/2-rate RSC encoder with a generating polynomial g = [5,7] and then passed through an Srandom interleaver. The coded bits are modulated in QPSK with a 10 kHz emission carrier frequency, and the symbol rate is set to 2.5 kSymbols/s.…”

Section: Simulationsmentioning

confidence: 99%

“…Owing to its abominable propagation characteristics, such as a low speed of the acoustic signal, large attenuation and significant multipath interference, the UWA channel can be seen as one of the most challenging elements [6]. The Doppler effect and multipath propagation caused by boundary reflection are the fundamental factors causing channel distortion, for which the acoustic signal experiences time and frequency selectivity [7][8][9]. One of the most widely utilized approaches to mediate harsh inter-symbol interference (ISI) due to channel aberration is the use of a decisionfeedback equalizer (DFE) with a digital phase locked loop (DPLL) embedded in it [10].…”

Section: Introductionmentioning

confidence: 99%

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Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

Han,

Tao,

Zhang

et al. 2023

Applied Sciences

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The oceanic positioning, navigation and timing (PNT) network requires high-quality underwater acoustic message transmission. Turbo equalization technology has exhibited superior performance for underwater acoustic (UWA) communications compared with conventional channel equalizers. To overcome the performance reduction caused by severe doubly selective UWA channels, the virtual space-time diversity soft direct-adaptation turbo equalization is proposed for UWA communications. The proposed scheme improves the ability of the typical turbo equalizer to deal with both Doppler and multipath effects for time varying channels. We utilize a fractionally spaced soft interference cancellation equalizer (FS-SE) instead of a hard decision to constitute the soft-input soft-output (SISO) equalizer. Combined with another virtual time-reversal mirror equalizer component, we can obtain virtual space and time diversity with only a single receiving transducer and mitigate the error propagation phenomenon of the feedback filter. To satisfy the sparse UWA channel, the ℓp,q-PRLS algorithm is applied to adaptive updates for FS-SE. In the proposed scheme, an adjustable interpolator and digital phase-locked loop are embedded into the equalizer to overcome the residual Doppler frequency shift and recover the timing distortion. Results of simulations and field lake trial show that the proposed scheme achieves better performance than existing ones under the same equalizer order.

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Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

Han,

Tao,

Zhang

et al. 2023

Applied Sciences

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“…However, the UWAC channels are hostile due to the influence of surface waves, ocean currents, turbulence, etc., on the multipath interference, which can be severe and time-varying. 1,2 It can be difficult to obtain accurate estimates and reliable equalizations of the time-varying multipath channels when there is relative motion between the transmitter and the receiver since the channels can shift significantly. 3,4 Using acoustic communication technology, underwater communication can achieve extended communication lengths ranging from a few meters (m) to tens of kilometers (km) for low and high transmission frequencies.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, UWAC has presented significant challenges in promoting better reliability due to increased multipath and time‐varying Doppler effects. However, the UWAC channels are hostile due to the influence of surface waves, ocean currents, turbulence, etc., on the multipath interference, which can be severe and time‐varying 1,2 . It can be difficult to obtain accurate estimates and reliable equalizations of the time‐varying multipath channels when there is relative motion between the transmitter and the receiver since the channels can shift significantly 3,4 .…”

Section: Introductionmentioning

confidence: 99%

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Kapileswar,

Phani Kumar

2023

Int J Communication

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SummaryNowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.

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“…Using a DL and DNN model, deep information can be extracted from ship radiation noise spectrums. The researchers presented a competitive network of Deep Beliefs based on Deep Belief Networks (DBN) by combining DNN with competitive learning techniques [6][7].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok¹,

Latha²

2022

Preprint

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Due to the complex and interdependent world, underwater acoustic source detection is a difficult challenge. It can be extremely difficult to perform Underwater Acoustic Target Recognition (UATR) utilizing ship-radiated noise due to the complicated aquatic organisms. This paper presents a new deep neural network called Audio Perspective based RCNN (APRCNN), learned by initial underwater acoustic waves with Depth Wise Separable (DWS) Convolutional Deep Neural Network. The APRCNN would then be implemented for underwater acoustic signal prediction and classification. The proposed harvester learning features & integration layers of the SPDNN system were motivated by perceptual processing, and the time-dilated convolution was influenced by machine translation. In this article, the researchers provide a methodology for identifying underwater targets using a thick DNN model. The proposed framework was created to reallocate all previous feature maps to maximize the categorization rate while minimizing the calculation burden in various degraded scenarios. Additionally, the proposed technique allows using the actual audio signal in the transfer function as the right network input information rather than using time-frequency spectrum analyzer images. Based on research findings assessed on a real-world collection of passive sonar, our classification method beats other cutting-edge DNN systems & classic ML methods, achieving an accuracy level of 98.4% at 0-dB SNR.

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Influence of Temporal and Spatial Fluctuations of the Shallow Sea Acoustic Field on Underwater Acoustic Communication

Cited by 6 publications

References 21 publications

Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

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