Estimation of an approximated likelihood ratio for iterative decoding in impulsive environment

Dimanche, Vincent; Goupil, Alban; Clavier, Laurent; Gellé, Guillaume

doi:10.1109/wcnc.2016.7565031

Cited by 5 publications

(2 citation statements)

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“…To solve (7), one needs a received sequence Y as well as the corresponding transmitted one X. This is usually obtained thanks to the use of a training sequence [20]. However, this induces an increase in the signaling and a decrease in the useful data rate.…”

Section: Unsupervised Optimizationmentioning

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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Section: Unsupervised Optimizationmentioning

confidence: 99%

An Unsupervised LLR Estimation with unknown Noise Distribution

Mestrah

Savard

Goupil

et al. 2020

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

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“…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.…”

mentioning

confidence: 99%

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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