Ambient noise statistics

Webster, Roger

doi:10.1109/78.218152

Cited by 45 publications

(20 citation statements)

References 4 publications

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“…However, the transmission of underwater acoustic signals is severely disturbed by background noise [1]. In practice, experiments conducted over the last decades show that underwater acoustic noise has the characteristics of non-Gaussian [2,3]. So underwater acoustic channel is appropriate to be fitted by non-Gaussian noise channel.…”

Section: Introductionmentioning

confidence: 99%

Rate-compatible LDPC convolutional codes over non-gaussian noise channel

Liu

2020

MATEC Web Conf.

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This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of ratecompatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

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Section: Introductionmentioning

confidence: 99%

Rate-compatible LDPC convolutional codes over non-gaussian noise channel

Liu

2020

MATEC Web Conf.

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“…3 In such applications, one can expect that output of an unknown FIR system includes acoustic signals with a positive kurtosis such as human conversation, music or spike noise, that is, so called super-Gaussian acoustic signals. [10][11][12] It is especially important to assume such conditions in acoustic echo cancellers; it is pointed out that estimation of system coefficients becomes problematic when noise contaminates output of an unknown FIR system, and SNR (Signal to Noise Ratio) declines substantially. 13 In addition, most methods of FIR system identification deal only with unknown coefficients.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric Bayesian model for system identification based on a super‐Gaussian distribution

Tanji

Murakami

Kamata

2019

Elect Comm in Japan

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In the acoustic signal processing applications of finite impulse response (FIR) system identification, it is important to develop the identification method that is robust to super‐Gaussian noises. Moreover, the identification method that estimates the FIR coefficients and the order of the unknown system is required, because the order of the unknown system is unavailable in advance. Therefore, in this paper, we propose a nonparametric Bayesian (NPB) model for FIR system identification using a super‐Gaussian likelihood and the beta‐Bernoulli process. In the proposed NPB model, we employ the hyperbolic secant distribution for the likelihood function. Then, we derive the inference algorithm to simultaneously estimate the FIR coefficients and the order of the unknown system. Our inference algorithm based on a hybrid inference approach combining the majorization‐minimization (MM) algorithm and the Gibbs sampler. The simulation results suggest that the proposed method outperforms the conventional identification algorithms in a super‐Gaussian noise environment.

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“…As to the additive noise, such a disturbance has been classically modeled as a possibly correlated Gaussian random process. However, the number of studies in the past few decades has shown, through both theoretical considerations and experimental results, that Gaussian random processes, even though they represent a faithful model for the thermal noise, are largely inadequate to model the effect of real-life noise processes, such as atmospheric and man-made noise (Kassam, 1988& Webster, 1993 arising, for example, in outdoor mobile communication systems. It has also been shown that non-Gaussian disturbances are commonly encountered in indoor environments, for example, offices, hospitals, and factories (Blankenship & Rappaport, 1993), as well as in underwater communications applications (Middleton, 1999).…”

Section: Diversity Problems In Wireless Communication Systems With Famentioning

confidence: 99%

Generalized Approach to Signal Processing in Wireless Communications: The Main Aspects and some Examples

Tuzlukov¹

2012

Wireless Communications and Networks - Recent Advances

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Ambient noise statistics

Cited by 45 publications

References 4 publications

Rate-compatible LDPC convolutional codes over non-gaussian noise channel

Rate-compatible LDPC convolutional codes over non-gaussian noise channel

A nonparametric Bayesian model for system identification based on a super‐Gaussian distribution

Generalized Approach to Signal Processing in Wireless Communications: The Main Aspects and some Examples

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