A Geometry-Based Underwater Acoustic Channel Model Allowing for Sloped Ocean Bottom Conditions

Naderi, Meisam; Pätzold, Matthias; Hicheri, Rym; Youssef, Néji

doi:10.1109/twc.2017.2664829

Cited by 21 publications

(16 citation statements)

References 29 publications

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“…• It is generally considered safe to assume the sea surface as a flat plane in underwater acoustic wave propagation, but this is not the case for the seafloor. In the work done by Naderi et al [83], the up/down slopes in seabed have been taken into account. This important consideration is particularly essential in the case of shallow waters in the presence of coral reefs and plants, and cannot be neglected.…”

Section: E Underwater Channel Modelingmentioning

confidence: 99%

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Jahanbakht

Wang

Hanzo

et al. 2021

IEEE Commun. Surv. Tutorials

283

111

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The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-theart applications, tools, and techniques. Finally, we analyze of the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Section: E Underwater Channel Modelingmentioning

confidence: 99%

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Jahanbakht

Wang

Hanzo

et al. 2021

IEEE Commun. Surv. Tutorials

283

111

View full text Add to dashboard Cite

show abstract

“…Authors in [32] consider a random distribution of Scatterers and rough condition of the surface and bottom ocean. A new geometry-based channel model raises up [33] for shallow UAC in which slopped ocean bottom is considered. It's shown that even a small slopped bottom has a significant impact on the UACs statistical properties.…”

Section: B Dynamic Underwater Acoustic Propagation 1) Time-varying Distortionmentioning

confidence: 99%

Underwater Wireless Sensor Networks: A Survey on Enabling Technologies, Localization Protocols, and Internet of Underwater Things

et al. 2019

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Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

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“…Near the ocean surface, however, both the pressure and temperature are generally invariant, resulting in a constant sound speed [4]. Therefore, the shallow-water ocean environment is regarded as an isovelocity environment [24]. Fig.…”

Section: A a Geometry-based Channel Modelmentioning

confidence: 99%

“…, and the time delay τ nm L (t) denotes the travel time of the LoS macro-eigenray between the nth hydrophone and the mth transducer. Similar to [24], the DA and UA components of the time-variant channel impulse response are, respectively,…”

Section: αT αRmentioning

confidence: 99%

Shallow Underwater Acoustic Massive MIMO Communications

Gao

Sun

et al. 2021

IEEE Trans. Signal Process.

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The potential benefits of massive multiple-input multiple-output (MIMO) make it possible to achieve high-quality underwater acoustic (UWA) communications. Nevertheless, due to the wideband nature of UWA channels, existing massive MIMO techniques for radio frequency cannot be directly applied to UWA communications. This paper investigates a UWA massive MIMO system in the shallow-water environment, deploying large array apertures at both the transmitter and the receiver. We propose a beam-based UWA massive MIMO channel model and analyze its properties. Based on this model, we reveal that the transmit design for rate maximization can be performed in a dimensionreduced space related to the channel taps. Then, we prove that the beam-domain transmission is optimal to maximize the rate when with unlimited numbers of transducers. Furthermore, if the number of hydrophones also tends to infinity, the optimal power allocation can be obtained just by the water-filling algorithm and the corresponding rate positively correlates with the number of channel taps for the high signal-to-noise-ratio regime. Moreover, we devise a low-complexity algorithm to optimize the input covariance matrix for general cases. Simulation results illustrate the significant performance of the proposed algorithm and the high throughput achieved by massive MIMO.

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A Geometry-Based Underwater Acoustic Channel Model Allowing for Sloped Ocean Bottom Conditions

Cited by 21 publications

References 29 publications

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Underwater Wireless Sensor Networks: A Survey on Enabling Technologies, Localization Protocols, and Internet of Underwater Things

Shallow Underwater Acoustic Massive MIMO Communications

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