Signal Detection in Arctic Under-Ice Noise

Nielsen, P.A.; Thomas, John B.

doi:10.21236/ada204175

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…GG distributions can also model noise distributions that appear in non-standard wireless media. In Nielsen and B.Thomas (1987) the authors showed that Arctic under-ice noise is well modeled by members of the GG family. In Banerjee and Agrawal (2013) the GG family has been recognized as a model for the underwater acoustic channel where values of p = 2.2 and p = 1.6 have been found to model the ship transit noise and the sea surface agitation noise, respectively.…”

Section: Past Workmentioning

confidence: 99%

Analytical properties of generalized Gaussian distributions

et al. 2018

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The family of Generalized Gaussian (GG) distributions has received considerable attention from the engineering community, due to the flexible parametric form of its probability density function, in modeling many physical phenomena. However, very little is known about the analytical properties of this family of distributions, and the aim of this work is to fill this gap. Roughly, this work consists of four parts. The first part of the paper analyzes properties of moments, absolute moments, the Mellin transform, and the cumulative distribution function. For example, it is shown that the family of GG distributions has a natural order with respect to second-order stochastic dominance. The second part of the paper studies product decompositions of GG random variables. In particular, it is shown that a GG random variable can be decomposed into a product of a GG random variable (of a different order) and an independent positive random variable. The properties of this decomposition are carefully examined. The third part of the paper examines properties of the characteristic function of the GG distribution. For example, the distribution of the zeros of the characteristic function is analyzed. Moreover, asymptotically tight bounds on the characteristic function are derived that give an exact tail behavior of the characteristic function. Finally, a complete characterization of conditions under which GG random variables are infinitely divisible and self-decomposable is given. The fourth part of the paper concludes this work by summarizing a number of important open questions.

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Section: Past Workmentioning

confidence: 99%

Analytical properties of generalized Gaussian distributions

et al. 2018

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“…It has also been discussed that the GGD better approximates impulsive atmospheric noises in several communication scenarios [85], [86]. Moreover, additive noise that arises in non-standard wireless media, such as under-ice noise and sea-surface agitation noise in underwater acoustics can also be modeled using GGD [87], [88]. Therefore, GGD exhibits a more appropriate fit to the noise data collected over a varied range of physical channel conditions [89], [90].…”

Section: B Related Work and Motivationmentioning

confidence: 99%

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Kowshik

Raghavendra

Gurugopinath

et al. 2023

IEEE Open J. Veh. Technol.

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In this paper, we propose a long short-term memory-based deep learning (DL) architecture for signal detection in uplink and downlink rate-splitting multiple access systems with multi-carrier modulation, over Nakagami-m fading and generalized Gaussian noise (GGN). The proposed DL detector completely eliminates the need for the use of successive interference cancellation (SIC), which suffers from disadvantages such as error propagation. In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard SIC receivers such as the least squares detector and the minimum mean-squared error receiver, and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate (SER). Furthermore, we study the effects of the shaping parameter of GGN, hyperparameters of the DL network such as batch size and learning rate on the SER performance. INDEX TERMS Deep learning (DL), generalized Gaussian noise (GGN), long short-term memory (LSTM), non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA).ANAGHA K. KOWSHIK (Graduate Student Member, IEEE) received the bachelor's degree (B.

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Application of Passive Hydroacoustics in the Studies of Sea-Ice, Icebergs and Glaciers: Issues, Approaches and Future Needs

Głowacki

Moskalik

2014

Achievements, History and Challenges in Geophysics

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Signal Detection in Arctic Under-Ice Noise

Cited by 9 publications

References 6 publications

Analytical properties of generalized Gaussian distributions

Analytical properties of generalized Gaussian distributions

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Application of Passive Hydroacoustics in the Studies of Sea-Ice, Icebergs and Glaciers: Issues, Approaches and Future Needs

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