Blind Estimation of an Approximated Likelihood Ratio in Impulsive Environment

Mestrah, Yasser; Savard, Anne; Goupil, Alban; Clavier, Laurent; Gellé, Guillaume

doi:10.1109/pimrc.2018.8580788

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Especially the linear part adapt to low noise value corresponding to the Gaussian part and the 1/x part correspond to the tail of the interference, dominated by the sub-exponential component of the total noise. It is however more complex to estimated but recent proposals allow to address this task [25,111].…”

Section: Discussionmentioning

confidence: 99%

“…We introduce estimation algorithms to ensure their adaptation capabilities. We also extend the solution we introduced in [23] based on the Normal Inverse Gaussian (NIG) family and propose a receiver that directly estimates the log-likelihood ratio function [19,24,25]. 4 We finally evaluate through simulations the robustness of several receivers when the interference impulsiveness varies or when the noise model is changed in the case of linear, myriad, p-norm, NIG and LLR-based receivers.…”

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confidence: 99%

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Impulsive noise modeling and robust receiver design

Clavier

Peters

Septier

et al. 2021

J Wireless Com Network

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Interference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the different statistical models proposed for such an interference, that can generally be unified using the class of sub-exponential family of distributions, and its impact on the receiver design. Visualizing the optimal decision boundaries allows one to show the non linear effect induced by impulsive noise models, which explains the significant loss in receiver performance designed under the standard Gaussian approximation. This motivates the need to develop new receivers. We propose a framework to design receivers robust to a variety of interference types, both Gaussian and non-Gaussian. We explore three ways of thinking about such receiver designs: a linear approach; by approximating the noise plus interference distribution; and by mimicking the decision rule distribution directly. Except for the linear approach, the other designs are capable of replicating the non-trivial optimal decision regions to different extents. The new detection algorithms are evaluated via Monte Carlo simulations. We focus on four efficient architectures, including the parameter estimations: Myriad, Normal Inverse Gaussian, p-norm and a direct estimation of the likelihood ratio function. They exhibit good performance, close to the optimal, in a large range of situations demonstrating they may be considered as robust decision rules in the presence of heavy tailed or impulsive interference environments.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Impulsive noise modeling and robust receiver design

Clavier

Peters

Septier

et al. 2021

J Wireless Com Network

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“…When y is close to zero, the LLR is almost linear, whereas when y is large enough, the LLR presents a power-law decrease. The presence of these two parts has been used in the literature to propose several LLRs [12,18,[27][28][29] and justifies the proposed piece-wise affine set for the LLRs approximation.…”

Section: Llr Approximation Under Impulsive Noisementioning

confidence: 90%

“…In the latter, instead of building a training sequence X at the decoder, we directly use the learning sequence to estimate the optimal θ * . More details on the supervised optimization can be found in our previous work [29].…”

Section: Estimation In Additive Sαs Noisementioning

confidence: 99%

An Unsupervised LLR Estimation with unknown Noise Distribution

Mestrah

Savard

Goupil

et al. 2020

J Wireless Com Network

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Many decoding schemes rely on the log-likelihood ratio (LLR) whose derivation depends on the knowledge of the noise distribution. In dense and heterogeneous network settings, this knowledge can be difficult to obtain from channel outputs. Besides, when interference exhibits an impulsive behavior, the LLR becomes highly non-linear and, consequently, computationally prohibitive. In this paper, we directly estimate the LLR, without relying on the interference plus noise knowledge. We propose to select the LLR in a parametric family of functions, flexible enough to be able to represent many different communication contexts. It allows limiting the number of parameters to be estimated. Furthermore, we propose an unsupervised estimation approach, avoiding the need of a training sequence. Our estimation method is shown to be efficient in large variety of noises and the receiver exhibits a near-optimal performance.

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“…This led to approximation based methods for calculating the density functions [5], [6], where the impulsiveness parameter α is assumed to be known apriori. In [7], a supervised learning based method for estimating the parameters for the approximation proposed in [6] is developed. Estimating the impulsive behavior of heavy-tailed noise or the corresponding α parameter of the distribution requires a substantial number of observations [8].…”

Section: Introductionmentioning

confidence: 99%

Blind decoding in $α$-Stable noise: An online learning approach

Raj,

Kalyani

2019

Preprint

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A novel method for performing error control coding in Symmetric α−Stable noise environments without any prior knowledge about the value of α is introduced. We use an online learning framework which employs multiple distributions to decode the received block and then combines these results based on the past performance of each individual distributions. The proposed method is also able to handle a mixture of Symmetric α−Stable distributed noises. Performance results in turbo coded system highlight the utility of the work.

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Blind Estimation of an Approximated Likelihood Ratio in Impulsive Environment

Cited by 6 publications

References 15 publications

Impulsive noise modeling and robust receiver design

Impulsive noise modeling and robust receiver design

An Unsupervised LLR Estimation with unknown Noise Distribution

Blind decoding in $α$-Stable noise: An online learning approach

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