Robust clipping demapper for LDPC decoding in impulsive channel

Maad, Hassen Ben; Goupil, Alban; Clavier, Laurent; Gellé, Guillaume

doi:10.1109/istc.2010.5613845

Cited by 1 publication

(1 citation statement)

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“…Besides, if we consider a family defined by a limited number of parameters and easy to implement, both the estimation and implementation complexities are reduced. This work can thus be seen as a generalization of some previous works that dealt with an approximation of the LLR function [8][9][10]: if the soft limiter and the hole puncher [11] are probably the best-known solutions, we previously proposed the approximation function f (y) = sgn(y) min(a|y|, b/|y|) [12], where sgn(y) extracts the sign of y. Nevertheless, we will not focus in this paper on the best family choice but on a generic way to estimate the approximation parameters in an unsupervised manner.…”

Section: Introductionmentioning

confidence: 96%

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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