Robust Clipping for OFDM Transmissions over Memoryless Impulsive Noise Channels

Tseng, Der-Feng; Han, Yunghsiang S.; Mow, Wai Ho; Chang, Li-Chung; Vinck, A. J. Han

doi:10.1109/lcomm.2012.050412.120491

Cited by 51 publications

(32 citation statements)

References 9 publications

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“…Earlier research on impulsive noise has considered simple noise model, i.e., Bernoulli-Gaussian model, -mixture model [5], [14], [15]. Due to their simplicity of the models, it was mathematically feasible to compute a threshold, by which impulsive noise is blocked.…”

Section: Preassumptions For Adaptive Threshold Blankermentioning

confidence: 99%

Adaptive Threshold Blanker in an Impulsive Noise Environment

Nam

Park

2014

IEEE Trans. Electromagn. Compat.

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This paper proposes a blanker with a threshold value that is adaptively updated based on average signal power in an impulsive noise environment. For conventional receivers, using a blanker as a preprocessor improves the bit error performance in an impulsive noise environment by removing received signal samples with large amplitudes in order to reduce the effect of impulsive noise. However, the conventional blanker shows a poor bit error performance in case of high signal-to-noise ratio (or large signal power) due to a fixed threshold. Therefore, the proposed blanker, called adaptive threshold blanker, overcomes this problem by adaptively updating the threshold and achieves an excellent bit error performance. In addition, this paper also introduces a simple method for calculating the quasi-optimal threshold value for the proposed blanker. This simple method allows an easy calculation of the threshold in practical systems. In order to evaluate the bit error performance of the proposed blanker, an approximated probability density function (PDF) using a sum of weighted Gaussian PDFs with different variances is also discussed. It is observed that simulation results agree well with the performance analysis.Index Terms-Adaptive blanker, impulsive noise, Middleton class A noise.

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Section: Preassumptions For Adaptive Threshold Blankermentioning

confidence: 99%

Adaptive Threshold Blanker in an Impulsive Noise Environment

Nam

Park

2014

IEEE Trans. Electromagn. Compat.

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“…To overcome the drawback of conventional decoder metric subjected to impulse noise, [14], [15] show the effectiveness of a limiter placed at the front-end receiver to suppress impulse noise in OFDM system. Moreover, despite lacking of the impulse statistics, the turbo decoder [14], [15], when accommodating the effect on the signal-to-noise ratio due to additional operation by a limiter, can significantly enhance the coding gain.…”

Section: Decoder Featured With Clipped-on Trellismentioning

confidence: 99%

“…Moreover, despite lacking of the impulse statistics, the turbo decoder [14], [15], when accommodating the effect on the signal-to-noise ratio due to additional operation by a limiter, can significantly enhance the coding gain. Instead of using a limiter, this work attempts to address the impulse noise by directly incorporating a clipping feature into the metric of turbo decoder.…”

Section: Decoder Featured With Clipped-on Trellismentioning

confidence: 99%

“…These facts provide the motivation for this study to address the impulse noise in a turbo-coded single-carrier system experiencing the very hostile environment, namely a high probability of occurrence of impulses characterized with a power greater than that of the background noise. Without resorting to the statistics of impulse noise, a limiter with a clipping threshold designed by Tseng et al [14], [15] in an OFDM system can effectively mitigate impulse. The turbo decoder there can also leverage the coding gain with the decoder metric adequately modified.…”

Section: Introductionmentioning

confidence: 99%

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Robust turbo decoding in impulse noise channels

Tseng

Tsai

Han

2013

2013 IEEE 17th International Symposium on Power Line Communications and Its Applications

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Powerline communications, an enabling technology for data networking alternative, susceptible to impulse noise such that it is likely to miss an assured quality of service. Turbo coding has long served as an essential tool to overcome this obstacle. However, this comes at the cost of estimating the statistics of impulse noise, which is generally not timeinvariant and may change rapidly over time, further increasing receiver complexity. This study proposes clipping on trellis to avoid this problem. A corresponding decoder metric dictated by the clipping threshold, which is a function of the probability of guessing for the unknown impulse arrival probability, was properly formulated to leverage the coding gain. Regardless of the impulse noise model assumed, the simulation results in this study, in terms of bit error probability, show that the proposed decoding scheme is robust to impulse and performs fairly close to its sophisticated counterpart, which however fully exploits the statistics of the impulse noise.

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“…For example, the high amplitude and the short duration of IN are considered as the main features for threshold based IN mitigation approaches. Memoryless nonlinear approaches such as clipping [2], blanking [3], and multiple-threshold blanking/clipping [4] are the most common methods in this category. In [5], a threshold optimization based on Neyman-Pearson criterion is proposed and an analytical equation for the quasi-optimal blanking and clipping thresholds is provided in [6].…”

Section: Introductionmentioning

confidence: 99%

Impulsive Noise Detection in OFDM-based Systems: A Deep Learning Perspective

Barazideh

Niknam

Natarajan

2019

2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)

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Efficient removal of impulsive noise (IN) from received signal is essential in many communication applications. In this paper, we propose a two stage IN mitigation approach for orthogonal frequency-division multiplexing (OFDM)-based communication systems. In the first stage, a deep neural network (DNN) is used to detect the instances of impulsivity. Then, the detected IN is blanked in the suppression stage to alleviate the harmful effects of outliers. Simulation results demonstrate the superior bit error rate (BER) performance of this approach relative to classic approaches such as blanking and clipping that use threshold to detect the IN. We demonstrate the robustness of the DNN-based approach under (i) mismatch between IN models considered for training and testing, and (ii) bursty impulsive environment when the receiver is empowered with interleaving techniques.Index Terms-Impulsive noise (IN), machine learning, deep neural network (DNN), orthogonal frequency-division multiplexing (OFDM).

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Robust Clipping for OFDM Transmissions over Memoryless Impulsive Noise Channels

Cited by 51 publications

References 9 publications

Adaptive Threshold Blanker in an Impulsive Noise Environment

Adaptive Threshold Blanker in an Impulsive Noise Environment

Robust turbo decoding in impulse noise channels

Impulsive Noise Detection in OFDM-based Systems: A Deep Learning Perspective

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