Clipping Demapper for LDPC Decoding in Impulsive Channel

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

doi:10.1109/lcomm.2013.031913.130060

Cited by 12 publications

(4 citation statements)

References 9 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: 87%

“…Other examples of piece-wise affine LLR approximations can be found in the literature. A classical solution is the clipping demapper [18] defined as L θ (x) = sgn(x) min(θ 1 |x|, θ 2 ). Nevertheless, other approximations that do not belong to our considered piece-wise affine function can also be found, as for instance the Hole puncher demapper [19] or non-linear approximation like in [8].…”

Section: Llr Approximation and Optimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

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: 87%

Section: Llr Approximation and Optimization Problemmentioning

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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“…This idea leads to a modification of the LLR function and classical examples are the soft limiter and the hole puncher [66,73,92,100,101]. For small received samples, a linear function is used and for large samples, respectively, a constant value or a zero are used as output of the LLR function.…”

Section: Llr Inspired Solutionsmentioning

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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“…Another popular approach to protect against impulsive noise is based on error correction code (ECC) including low-density parity-check codes (LDPC) [23], LDPC convolutional codes [24] and codes turbo codes (TC) [25] for the following two reasons. They can provide results closed to Shannon limit capacity with acceptable complexities and the check relationships of block codes can embody the constraints, which help to suppress the additive noises caused by PLC channels.…”

Section: Related Workmentioning

confidence: 99%

OFDM-based power-line communication enhancement using a turbo coded adaptive impulsive noise compensator

Himeur

Boukabou

2015

Telecommun Syst

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In this paper, we propose a novel estimation and decoding scheme for power-line communication (PLC) systems in impulsive noise environments. The proposed scheme is based on the turbo coding combined with adaptive noise compensation to reduce burst errors and multipath effects. For this purpose, the PLC channel and noise models are introduced, then, the turbo encoder/decoder are inserted in the mapper/demapper and the pilot insertion block for the sake of enhancing preliminary estimation of the transmitted orthogonal frequency division multiplexing signals. The proposed impulsive noise compensator is based on the estimation of the impulse bursts using a new blanking/clipping function, and on the estimation of the signal to impulse noise ratio and the peak to average power ratio. Simulation results illustrate that receivers with combined turbo coding and the proposed noise compensator drastically outperform existing receivers under impulsive noise. In comparison to some existing schemes, the proposed scheme reaches its perfect performance in a reduced 15-paths environment, when the bit error rate and mean square error performance are tested. The improvements in SNR performance are more than 16 dB for BPSK modulation and can reach 12-20 dB for 16-QAM modulation, when a high impulsive noise level is considered.

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Clipping Demapper for LDPC Decoding in Impulsive Channel

Cited by 12 publications

References 9 publications

An Unsupervised LLR Estimation with unknown Noise Distribution

An Unsupervised LLR Estimation with unknown Noise Distribution

Impulsive noise modeling and robust receiver design

OFDM-based power-line communication enhancement using a turbo coded adaptive impulsive noise compensator

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