Time-Scale and Dispersive Processing for Wideband Time-Varying Channels

Papandreou-Suppappola, Antonia; Ioana, Cornel; Zhang, Jun Jason

doi:10.1016/b978-0-12-374483-8.00009-1

Cited by 4 publications

(7 citation statements)

References 70 publications

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“…The Doppler-scale spread considered within each path group was computed using (19) to be δv = ±0.16 m/s, resulting in 2 Doppler scale factors. The WSF recovery error within each path group was ε 1 = 3.59%, ε 2 = 6.04%, EURASIP Journal on Advances in Signal Processing 11 3 3.5 4 4.5 5 ε 3 = 26.12%, and ε 4 = 18.31%; the total recovery error was ε = 5.27% on the extracted signals. Note that most of the received signal energy was contained within the first two arrival paths; the last two arrival paths had a lower SNR and thus were more difficult to characterize.…”

Section: Wsf Estimation Using the Discrete Time-scalementioning

confidence: 96%

“…The wideband LTV channel model represents the channel output in terms of continuous time-delay and Doppler-scale change transformations on the transmitted signal, weighted by the WSF. Specifically, the noiseless received signal x(t) can be represented as [1,3,8,[18][19][20]…”

Section: Discrete Time-scale Channel Characterizationmentioning

confidence: 99%

“…Most physical systems can be represented by models that account for the transformations caused on the propagating signal. Depending on these transformations, linear timevarying (LTV) systems have been represented using narrowband, wideband; or dispersive nonstationary models [1][2][3]. Although all LTV systems can be characterized by a kernel representation of their time-varying impulse response [4,5], they can also be identified by a matched spreading function that can provide a physical interpretation of the system effects on the propagating signal [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The first proposed characterization method estimates the coefficients of the smoothed and sampled WSF based on a discrete version of the wideband LTV system representation. The discrete canonical model was initially proposed to improve efficiency in processing and provide improved performance using model-inherent diversity paths [3,8,20,21]. The model decomposes the received signal into a linear combination of time-shifted and Doppler-scaled versions of the transmitted signal, weighted by a smoothed and sampled version of the WSF.…”

Section: Introductionmentioning

confidence: 99%

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Nonstationary System Analysis Methods for Underwater Acoustic Communications

Josso

Zhang

Papandreou-Suppappola

et al. 2011

EURASIP J. Adv. Signal Process.

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The underwater environment can be considered a system with time-varying impulse response, causing time-dependent spectral changes to a transmitted acoustic signal. This is the result of the interaction of the signal with the water column and ocean boundaries or the presence of fast moving object scatterers in the ocean. In underwater acoustic communications using mediumto-high frequencies (0.3-20 kHz), the nonstationary transformation on the transmitted signals can be modeled as multiple timedelay and Doppler-scaling paths. When estimating the channel, a higher processing performance is thus expected if the techniques used employ a matched channel model compared to those that only compensate for wideband effects. Following a matched linear time-varying wideband system representation, we propose two different methods for estimating the underwater acoustic communication environment. The first method follows a canonical timescale channel model and is based on estimating the coefficients of the discrete wideband spreading function. The second method follows a ray system model and is based on extracting timescale features for different ray paths using the matching pursuit decomposition algorithm. Both methods are validated and compared using communication data from actual underwater acoustic communication experiments. spreading function can provide a means for improving communication receiver performance [11, 12]. Characterizing acoustic signal propagation through water is essential for many applications, including underwater acoustic communications, active and passive sonar, underwater navigation and tracking, and ocean acoustic tomography. The highly time-varying nature of the underwater environment can cause many undesirable distortions to the propagating signal. Time-varying multipath distortions may be the result of dense reflections from rough surfaces, fluctuations in sound speed due to inhomogeneous mediums, relative motion between transmitters and receivers, or changes in the propagating medium [13]. Depending on the transmission frequency and ocean depth, the time-dependent spectral changes in the signal can be Doppler scaling (compression or expansion) or dispersive (nonlinear) transformations [3, 6, 14]. In particular, medium-to-high frequency (0.3-20 kHz) underwater acoustic signals are characterized by spreading caused by multiple time-delay paths and multiple Doppler-scaling

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Section: Wsf Estimation Using the Discrete Time-scalementioning

confidence: 96%

Section: Discrete Time-scale Channel Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonstationary System Analysis Methods for Underwater Acoustic Communications

Josso

Zhang

Papandreou-Suppappola

et al. 2011

EURASIP J. Adv. Signal Process.

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“…OFDM and TF dispersive channels are at the heart of a broad range of communication systems, including Digital Audio/Video Broadcasting, Wireless Local Area Networks (IEEE 802.11), Wireless Metropolitan Area Networks (IEEE 802. 16), 3GPP Long-Term Evolution, Wireless Personal Area Networks (e.g., WiMedia), Vehicular Ad Hoc Networks, Lband Digital Aeronautical Communication Systems, Digital Subscriber Lines, power-line communications, and underwater acoustic communications [5], [16]- [21]. The purpose of this paper is to discuss the relevance of TF analysis to OFDM and TF dispersive channels, and to demonstrate that WH frame theory [22] and TF operator representations are powerful tools HISTORY TF analysis has been linked with communications for a long time.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Foundations of Communications: Concepts and Tools

Matz

Bölcskei

Hlawatsch³

2013

IEEE Signal Process. Mag.

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Hitherto communication theory was based on two alternative methods of signal analysis. One is the description of the signal as a function of time; the other is Fourier analysis. Both are idealizations, as the first method operates with sharply defined instants of time, the second with infinite wave-trains of rigorously defined frequencies. But our everyday experiences-especially our auditory sensations-insist on a description in terms of both time and frequency. -Dennis Gabor [1]

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Identification of System with Non-stationary Signal Using Modified Wilcoxon Approach

Dash

Mohanty

2013

Advances in Intelligent Systems and Computing

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Time-Scale and Dispersive Processing for Wideband Time-Varying Channels

Cited by 4 publications

References 70 publications

Nonstationary System Analysis Methods for Underwater Acoustic Communications

Nonstationary System Analysis Methods for Underwater Acoustic Communications

Time-Frequency Foundations of Communications: Concepts and Tools

Identification of System with Non-stationary Signal Using Modified Wilcoxon Approach

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