Nonlinear and non-Gaussian ocean noise

Brockett, Patrick L.; Hinich, Melvin J.; Wilson, Gary R.

doi:10.1121/1.395273

Cited by 99 publications

(26 citation statements)

References 7 publications

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“…The mean vector of Z is given by µ = E Z . In distinction from (20), the covariance matrix of Z is defined in multi-dimensional complex space Σ ∈ C m×m , that is…”

Section: A Notationsmentioning

confidence: 99%

McLeish Distribution: Performance of Digital Communications Over Additive White McLeish Noise (AWMN) Channels

Yılmaz

2020

IEEE Access

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The objective of this article is to statistically characterize and describe a more general additive noise distribution, termed as McLeish distribution, whose random nature can model different impulsive noise environments often encountered in practice and provides a robust alternative to Gaussian distribution. Accordingly, we develop circularly and elliptically symmetric multivariate McLeish distribution and introduce additive white McLeish noise (AWMN) channels. In particular, we propose novel analytical and closed-form expressions for the symbol error rate (SER) performance of coherent / non-coherent signaling using various digital modulation techniques over AWMN channels. In that context, we illustrate some novel expressions by some selected numerical examples and verify them by some well chosen computer-based simulations.

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“…The mean vector of Z is given by µ = E Z . In distinction from (20), the covariance matrix of Z is defined in multi-dimensional complex space Σ ∈ C m×m , that is…”

Section: A Notationsmentioning

confidence: 99%

McLeish Distribution: Performance of Digital Communications Over Additive White McLeish Noise (AWMN) Channels

Yılmaz

2020

IEEE Access

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“…x f x f (7) where κ ≅ 42.3423. Figure 3 shows the comparison of the relative frequency of the noise samples and the estimated distribution.…”

Section: Noise Modelmentioning

confidence: 99%

“…Finally, changing the variables in (7) and applying the result in (10), it follows that the symbol error probability of the binary UWAN channel can be estimated as…”

Section: Error Performance Analysismentioning

confidence: 99%

Underwater Acoustic Noise Model for Shallow Water Communications

Panaro¹,

Lopes²,

Barreira³

et al. 2012

Anais De XXX Simpósio Brasileiro De Telecomunicações

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Resumo-Este artigo apresenta um modelo empírico para o ruído do canal acústico submarino em águas rasas a partir da análise de dados provenientes de medições realizadas em campo. Uma função de densidade de probabilidade para a distribuição da amplitude do ruído é proposta e funções de verossimilhança são obtidas. Em decorrência, uma expressão para a probabilidade de erro de símbolo para sinalização binária no canal é apresentada. Além disso, são fornecidos os resultados de simulação realizada com as amostras reais de ruído coletadas em campo, de modo a verificar o efeito do ruído no desempenho de sistemas acústicos de comunicação submarina com sinalização binária. Palavras-Chave-Comunicação Submarina, Ruído Acústico Submarino, Distribuição de Ruído.Abstract-This article presents an empirical model for the noise of the acoustic underwater channel in shallow water from the analysis of field data measurements. A probability density function for the noise amplitude distribution is proposed and the associated likelihood functions are derived. As a result, an expression to the probability of symbol error for binary signaling is presented for the channel. Additionally, the results of simulations conducted using the field collected noise samples are presented, in order to verify the noise effect on the performance of underwater acoustic communication binary signaling systems.

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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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Nonlinear and non-Gaussian ocean noise

Cited by 99 publications

References 7 publications

McLeish Distribution: Performance of Digital Communications Over Additive White McLeish Noise (AWMN) Channels

McLeish Distribution: Performance of Digital Communications Over Additive White McLeish Noise (AWMN) Channels

Underwater Acoustic Noise Model for Shallow Water Communications

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

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