On Detection Method for Soft Iterative Decoding in the Presence of Impulsive Interference

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

doi:10.1109/lcomm.2014.2320921

Cited by 24 publications

(15 citation statements)

References 11 publications

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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: 85%

“…, θ n ], is the optimization parameter that needs to be estimated. This approach was already investigated in previous works [8,12]. In [12], we considered the case of two parameters, which showed good results according to bit error rate (BER).…”

Section: Llr Approximation and Optimization Problemmentioning

confidence: 99%

“…It could however be different for other approximation families but the best optimization method as well as its complexity study remain out of the scope of this paper. [12], the approximation L θ (x) = sgn(x) min(θ 1 |x|, θ 2 /|x|). Other examples of piece-wise affine LLR approximations can be found in the literature.…”

Section: Llr Approximation and Optimization Problemmentioning

confidence: 99%

“…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: 99%

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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: Llr Approximation Under Impulsive Noisementioning

confidence: 85%

Section: Llr Approximation and Optimization Problemmentioning

confidence: 99%

Section: Llr Approximation and Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Unsupervised LLR Estimation with unknown Noise Distribution

Mestrah

Savard

Goupil

et al. 2020

J Wireless Com Network

Self Cite

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“…To solve the problem, an enhanced demapping algorithm modifies the constellation symbols with the consideration of reconstructed nonlinear distortion to improve the demapping extrinsic log likelihood ratio (LLR), which however consumes numerous complexity [10]. In [11], an approximation of the LLR is derived in the case of α-stable impulsive interferences. In [12], a doubly iterative receiver with pulse blanking is designed to overcome the impulsive interference, including an inner loop and an outer loop.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Learning for LDPC Coded BICM Receivers With Blanking Nonlinearity

Jiang

Zhao

2019

IEEE Access

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With the aid of blanking nonlinearity, the low density parity check (LDPC) coded bitinterleaved coded modulation (BICM) has been jointly considered as a robust mitigation for the impulsive interference in orthogonal frequency division multiplexing (OFDM) systems. However, the Gaussian assumption for the nonlinear channel conditional probability induces the mismatched L-values in the conventional MAP demodulator. In this paper, combined with the pulse blanking optimization via the PEXIT analysis, we propose a novel MAP demodulator based on the Gaussian mixture model (GMM) and estimate the parameters with the expectation-maximization (EM) algorithm. Taking the L-band Digital Aeronautical Communication System Type1 (L-DACS1) as an example, the GMM-based MAP demodulator can obtain the PEXIT thresholds that match the decoding curves well and provide the better BER performance in the interference-limit channel environment.

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