Modeling the large scale transmission loss in underwater acoustic channels

Qarabaqi, Parastoo; Stojanovic, Milica

doi:10.1109/allerton.2011.6120201

Cited by 37 publications

(18 citation statements)

References 10 publications

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“…studies are usually based on the analyses of experimental acoustic data collected in a particular location. Some authors find Ricean fading [4,6,7] or Rayleigh fading [1] to provide a good match for their measurements, while others find log-normal distribution [5,8] or the K-distribution [9,10], to be a better fit. The variety of the proposed statistical models is due to experiment-specific properties, e.g.…”

Section: Introductionmentioning

confidence: 95%

“…Numerous studies have been conducted to stochastically model the UWA channel, e.g. [1,[4][5][6][7][8][9][10]. These Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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Small scale characterization of underwater acoustic channels

Qarabaqi

Stojanovic

2012

Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems - WUWNet '12

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Underwater acoustic (UWA) channel models provide a tool for predicting the performance of communication systems prior to system deployment and are thus essential for system design. Small-scale modeling of the UWA channel targets statistical characterization of random effects such as scattering and motion-induced Doppler shifting. In this study, each transmission path is modeled as consisting of micromultipath components that cumulatively result in a complex Gaussian multiplicative distortion, whose time-and frequency-correlation are assessed analytically. Motion of the surface and transmitter/receiver displacements introduce additional variation whose temporal correlation is modeled via Bessel-type functions. Data obtained from three underwater experiments support the proposed model.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Small scale characterization of underwater acoustic channels

Qarabaqi

Stojanovic

2012

Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems - WUWNet '12

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“…1. The path gains are calculated using frequencydependent propagation loss and plane wave propagation model [8]. The surface reflection coefficients are set to -1 and the bottom reflection coefficients are evaluated considering the incident angle and the speed of sound in the the seabed.…”

Section: Performance Analysis Through Simulationmentioning

confidence: 99%

A method for differentially coherent multichannel processing of acoustic OFDM signals

Aval

Stojanovic

2012

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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We propose a low complexity array processing method for differential detection of OFDM signals over underwater acoustic channels. Partial FFT technique, developed previously for mitigating inter-carrier interference in singlechannel receivers operating over time-varying channels, is extended to multichannel configuration. Performance results based on simulation and experimental data demonstrate the advantage of the proposed method compared to conventional differential and coherent detection.

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“…The solid curve represents the log-distance model (5), whose parameters g 0 and k 0 were obtained by first-order polynomial fitting. 3 We emphasize again that the model parameters will in general depend on the operational conditions [28], i.e. that the values indicated in the figure are representative of the 8-12 kHz acoustic band and transmission distances on the order of several hundreds of meters.…”

Section: B Acoustic Link Gain Modelmentioning

confidence: 99%

Underwater Data Collection Using Robotic Sensor Networks

Hollinger

Choudhary

Qarabaqi

et al. 2012

IEEE J. Select. Areas Commun.

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171

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Abstract-We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multinode communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.

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Modeling the large scale transmission loss in underwater acoustic channels

Cited by 37 publications

References 10 publications

Small scale characterization of underwater acoustic channels

Small scale characterization of underwater acoustic channels

A method for differentially coherent multichannel processing of acoustic OFDM signals

Underwater Data Collection Using Robotic Sensor Networks

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