Locally Optimal Inspired Detection in Snapping Shrimp Noise

Mahmood, Ahmed; Vishnu, Hari

doi:10.1109/joe.2017.2731058

Cited by 20 publications

(5 citation statements)

References 15 publications

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“…To further adapt our approach to the underwater context, the additive noise is assumed to be possibly impulsive or transient [45]- [48]. This type of noise can be generated by man-made activities, biological or geophysical sources and is sometimes caused by faulty elements in the receiver front-end.…”

Section: Application 2: Signal Detection In Impulsive Noisementioning

confidence: 99%

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

Socheleau

2024

IEEE J. Oceanic Eng.

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The effect of underwater acoustic propagation on the cyclostationary features of communication signals is modeled and analyzed. Two kinds of channels are considered: the multiscale-multilag channel, over which mobile and wideband acoustic systems usually communicate, and the dispersive channel resulting from low-frequency modal propagation in shallow water. It is shown that multiscale-multilag channels transform cyclostationary signals into a sum of velocity and acceleration-dependent timewarped cyclostationary processes. This time-warping is carefully taken into account to efficiently recover the cyclostationary features. On the other hand, it is found that low-frequency dispersive channels preserve the original periodicity but attenuate the shorter cycles and spread the correlations. To illustrate the theoretical results, applications with simulated and real data are also presented. Specifically, the problem of estimating time-varying Doppler scales is addressed for multiscale-multilag channels as well as the detection of signals with unique cyclostationary signatures. The example of blind symbol-rate estimation applied to covert communications in dispersive channels is also discussed. Special attention is paid to PSK, QAM, OFDM and DSSS signals. Accompanying supplementary material provides the MATLAB code used for the estimation and detection examples.

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Section: Application 2: Signal Detection In Impulsive Noisementioning

confidence: 99%

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

Socheleau

2024

IEEE J. Oceanic Eng.

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“…2) Underwater Acoustic Communications: The impulsive noise in underwater acoustic communications systems is typically caused by natural sources (including bio-acoustic sounds, water agitation and crustal movement) and human activities (for example, shipping, oil and gas exploration and production) [232]. Among these noise components, the snapping shrimp noise plays a dominant role [233]. The noise process is usually modeled as a stationary α-sub-Gaussian noise associated with a memory order m [233], which is essentially an impulsive Markov process of order m and can be understood with the aid of the discussions in Section III-C2.…”

Section: Extensions To Other Communications Areasmentioning

confidence: 99%

“…Among these noise components, the snapping shrimp noise plays a dominant role [233]. The noise process is usually modeled as a stationary α-sub-Gaussian noise associated with a memory order m [233], which is essentially an impulsive Markov process of order m and can be understood with the aid of the discussions in Section III-C2. In order to simplify the performance analysis and system design, simple mathematical models, such as the Gaussian Mixture model detailed in Section III-C1, have also been considered in noise modeling [234].…”

Section: Extensions To Other Communications Areasmentioning

confidence: 99%

“…In order to simplify the performance analysis and system design, simple mathematical models, such as the Gaussian Mixture model detailed in Section III-C1, have also been considered in noise modeling [234]. The state-of-the-art in noise mitigation in underwater acoustic communications can be classified into nonlinear preprocessing [232], symbol detection [233], and compressedsensing-aided noise mitigation [235]. More advanced hybrid noise mitigation relying on combining the techniques detailed in Section IV is capable of further enhancing the robustness of underwater acoustic systems against impulsive noise.…”

Section: Extensions To Other Communications Areasmentioning

confidence: 99%

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Fifty Years of Noise Modeling and Mitigation in Power-Line Communications

Bai

Zhang

Wang

et al. 2021

IEEE Commun. Surv. Tutorials

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Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and nonparametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC. Index Terms-Power-line communications, background noise, impulsive noise, narrow-band interference, noise modeling and mitigation techniques. I. INTRODUCTION A. Power-Line Communications Power-line communications (PLC) has been considered as a means of data transmission since the late 19th century [1, Ch. 1]. Early applications include narrow-band voice and data communications over both medium-and high-voltage power lines for telemetry, telecontrol and teleprotection purposes, as the predecessors of to contemporary smart grid communications [2]-[6]. However, communications over power-lines have not received wider attention until the late 1990s [7], when both the telecommunications services and the electricity industry T. Bai, M. Elkashlan and A. Nallanathan are with the School of Electronic Engineering and Computer Science,

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“…which leads to (14) directly after taking expectation. Substituting (74) into the right side of the following formula…”

Section: Appendix B Proof Of Lemmamentioning

confidence: 99%

Performance Analysis of Gini Correlator for Detecting Known Signals in Impulsive Noise

Chen

Dai

et al. 2019

IEEE Access

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Detection of known signals embedded in additive noise is a fundamental problem in signal processing. For normally distributed noise, it is well known that the popular matched filter detector (MFD) is optimal. However, for impulsive noise whose distribution has a heavier tail, the performance of MFD will deteriorate severely. To deal with such problem, in this paper, we propose a novel detector, termed as Gini correlator (GC), and derive the analytic forms of its expectation and variance, under a specified contaminated Gaussian model (CGM) emulating a frequently encountered scenario in practice. In order to further understand its properties, we compare the proposed GC with five state-of-the-art detectors permeating in the literature, in terms of Pitman asymptotic relative efficiency (ARE), as well as time-delay estimation. Monte Carlo simulations not only validate our theoretical discoveries, but also demonstrate the advantages of GC in the aspects of 1) accurate control of false alarm probability without prior knowledge of noise distribution, 2) comparable performance with MFD for Gaussian noise, 3) better performances over the classical detectors in the aspects of greater ARE and smaller bias and standard deviation for time-delay estimation. The theoretical and empirical findings in this work enable GC to be a useful alternative to the existing detectors whether or not impulsivity exists in noise. INDEX TERMS Gini correlator (GC), Kendall's tau (KT), locally optimal detector (LOD), Pitman asymptotic relative efficiency (ARE), sign correlator (SC), Spearman's rho (SR).

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Locally Optimal Inspired Detection in Snapping Shrimp Noise

Cited by 20 publications

References 15 publications

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications

Performance Analysis of Gini Correlator for Detecting Known Signals in Impulsive Noise

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