Statistical Characterization and Computationally Efficient Modeling of a Class of Underwater Acoustic Communication Channels

Qarabaqi, Parastoo; Stojanovic, Milica

doi:10.1109/joe.2013.2278787

Cited by 524 publications

(288 citation statements)

References 19 publications

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“…for the channel setup but we deliberately discard λ (11), to represent the case of adopting a traditional terrestrial RSSbased localization method for UWA scenarios. Not surprisingly, it yields the worst RMSE result in this test as shown in Fig.…”

Section: Computer Simulations and Discussionmentioning

confidence: 99%

“…We argue that although α (f ) is usually small [11] and thus P (∆) i may be too small to be sensible in practice, we can always quantify them by high-precision devices. Hence we rewrite (12) by taking d0 = 1 as…”

Section: Fdrss-based Approachmentioning

confidence: 99%

“…where the term 10βlog 10 d accounts for geometric spreading which increases with the propagation distance and is independent of frequency, with β ≥ 0 being the path-loss exponent (PLE); the term α (f ) d accounts for the frequency-dependent absorption by the medium in the Urick propagation model [15] with α (f ) being the absorption coefficient that can be obtained in dB per kilometer using Thorp's empirical formula as [11] 0.11 f…”

Section: System Modelmentioning

confidence: 99%

“…We notice that the deformation forms of the TL model (1) can be found widely in UWA research, e.g., (1) in [17], (1) in [11] and (8-32) in [18]. Utilizing the RSS as a measurement means that the average of the instantaneous received power is attained over several consecutive time slots and hence the small-scale fading can be neglected.…”

Section: System Modelmentioning

confidence: 99%

“…However, it can be argued that for certain water depths, the UWA channels show nice transmission features that fit well to a TL model and thus RSS-based localization can be considered for such cases [9]. To represent the TL features of an UWA channel, the Urick propagation model [10] is among the most popular, based on which some statistical models are derived [11]. Other UWA TL modeling methods can also be found, e.g., using Lambert W function [12].…”

Section: Introductionmentioning

confidence: 99%

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RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios.

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Section: Computer Simulations and Discussionmentioning

confidence: 99%

Section: Fdrss-based Approachmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Underwater Acoustic Communication

Stojanovic

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Underwater wireless communications over distances in excess of about 100 m are established using acoustic signals. Acoustic signals propagate as pressure waves, whose energy absorption limits the available bandwidth. As a result, existing technology provides bit rates on the order of several kilobits per second for transmission over distances on the order of several kilometers. Additional challenges are presented by multipath propagation that causes frequency selectivity, random time variation, and Doppler effects that occur due to low speed of sound (1500 m/s). This article overviews the development of acoustic modem technology, which evolved over the past several decades from noncoherent modulation/detection techniques to bandwidth‐efficient phase‐coherent modulation/detection. A survey of techniques for channel equalization in single‐carrier systems as well as recent advances in multicarrier acoustic communications is also presented.

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On the mobile‐to‐mobile linear time‐variant shallow‐water acoustic channel response

Sauco-Gallardo

Fernández‐Plazaola

Díez

2017

Int J Communication

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We expose some concepts concerning the channel impulse response (CIR) of linear time-varying (LTV) channels to give a proper characterization of the mobile-to-mobile underwater channel. We find different connections between the linear time-invariant (LTI) CIR of the static channel and two definitions of LTV CIRs of the dynamic mobile-to-mobile channel. These connections are useful to design a dynamic channel simulator from the static channel models available in the literature. Such feature is particularly interesting for overspread channels, which are hard to characterize by a measuring campaign. Specifically, the shallow water acoustic (SWA) channel is potentially overspread due the signal low velocity of propagation which prompt long delay spread responses and great Doppler effect. Furthermore, from these connections between the LTI static CIRs and the LTV dynamic CIRS, we find that the SWA mobile-to-mobile CIR does not only depend on the relative velocity between transceivers, but also on the absolute velocity of each of them referred to the velocity of * This work is under review for Journal publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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Statistical Characterization and Computationally Efficient Modeling of a Class of Underwater Acoustic Communication Channels

Cited by 524 publications

References 19 publications

RSS-based sensor localization in underwater acoustic sensor networks

RSS-based sensor localization in underwater acoustic sensor networks

Underwater Acoustic Communication

On the mobile‐to‐mobile linear time‐variant shallow‐water acoustic channel response

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