A Novel Multireceiver SAS RD Processor

Zhang, Xuebo; Yang, Peixuan; Wang, Yanmei; Shen, Wenyan; Yang, Jiachong; Wang, Junfeng; Ye, Kun; Zhou, Mingzhang; Sun, Haixin

doi:10.1109/tgrs.2024.3362886

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…As is described in Section 3.2, the seawater movement model is used to fit the movement of nodes to reflect the impact of seawater flow on the nodes. We obtain the velocity V x perpendicular to the coast, velocity V y along the coast, and velocity function y(v, t) of time t by Equation (9). The traditional WOA is based on the position vector → X, and the velocity in the velocity function is also a vector.…”

Section: Optimal Speed-based Relay Node Selection Algorithmmentioning

confidence: 99%

“…This results in a change in the topology of the network, the formation of the routing void [7,8], and the interruption of the of the original route. These factors lead superfluous data retransmission [9][10][11][12] in order to seek out the latest route. Furthermore, it is difficult to replace the battery [13] embedded in the underwater node, and the increase in data retransmission will frequently consume a large amount of node energy and reduce network life.…”

Section: Introductionmentioning

confidence: 99%

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Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Tan,

Li,

Sun

et al. 2024

JMSE

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An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption.

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Section: Optimal Speed-based Relay Node Selection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Tan,

Li,

Sun

et al. 2024

JMSE

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show abstract

“…While our proposed method demonstrates superior performance on classical underwater image super-resolution datasets (visible data) (Liu et al, 2024), its applicability is currently somewhat limited. The model's performance is significantly affected in scenarios with low light conditions, such as deep-sea environments or areas with poor visibility, where methods utilizing sonar (Yang, 2023;Zhang et al, 2024) for detection are more prevalent. To adapt our method to a wider range of underwater scenarios, we will explore the potential applications of the proposed method in sonar images.…”

Section: Limitationsmentioning

confidence: 99%

Learning hybrid dynamic transformers for underwater image super-resolution

He,

Li,

Jia

2024

Front. Mar. Sci.

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Underwater image super-resolution is vital for enhancing the clarity and detail of underwater imagery, enabling improved analysis, navigation, and exploration in underwater environments where visual quality is typically degraded due to factors like water turbidity and light attenuation. In this paper, we propose an effective hybrid dynamic Transformer (called HDT-Net) for underwater image super-resolution, leveraging a collaborative exploration of both local and global information aggregation to help image restoration. Firstly, we introduce a dynamic local self-attention to adaptively capture important spatial details in degraded underwater images by employing dynamic weighting. Secondly, considering that visual transformers tend to introduce irrelevant information when modeling the global context, thereby interfering with the reconstruction of high-resolution images, we design a sparse non-local self-attention to more accurately compute self-similarity by setting a top-k threshold. Finally, we integrate these two self-attention mechanisms into the hybrid dynamic transformer module, constituting the primary feature extraction unit of our proposed method. Quantitative and qualitative analyses on benchmark datasets demonstrate that our approach achieves superior performance compared to previous CNN and Transformer models.

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“…In UACs, underwater communications channels introduce multipath interference, leading to inter-symbol interference. In sonar imaging, this manifests as ghosts [5,6]. Initially, these problems were primarily addressed using algorithms like least squares and recursive least squares.…”

Section: Introductionmentioning

confidence: 99%

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Li,

Han,

2024

Remote Sensing

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Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.

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A Novel Multireceiver SAS RD Processor

Cited by 10 publications

References 33 publications

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Learning hybrid dynamic transformers for underwater image super-resolution

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

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