A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

Li, Jin; Feng, Da‐Zheng; Li, Bingbing

doi:10.1016/j.aeue.2017.08.036

Cited by 18 publications

(2 citation statements)

References 26 publications

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“…Since the second-order statistics no longer converge under α-stable distribution noise, the traditional signal-noise ratio function will lose its meaning under α-stable distribution noise. In order to assess the signal-noise ratio (SNR) of useful signals and pulse noise effectively, it is imperative to employ new methodologies [27]. This paper employs the generalized signal-noise ratio (Formula ( 15)) in evaluating the system's signal-noise ratio.…”

Section: Simulation Analysis Of the Proposed Algorithmmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm.

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Section: Simulation Analysis Of the Proposed Algorithmmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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“…ANNs applied for 256 QAM symbol channel equalization over the OFDM Rayleigh channel [ 44]. Li et.al, [45] presented a constant modulus algorithm (CMA) to restrain impulse noise adaptively. The CMA algorithm operates in a simple unsupervised manner and was shown to be effective for non-linear channels.…”

Section: Introductionmentioning

confidence: 99%

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Mohapatra¹,

Rout²,

Bisoy

et al. 2022

IEEE Access

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Channel equalization is remaining a challenge for the researcher. Especially for the non-linear channel as well as the extremely dispersive channel, an effective channel equalizer is required. It is common knowledge that non-linear channel equalizers based on the neural networks (NN) outperform adaptive filter-based linear equalizers. To train NN equalizers, gradient-descent-based approaches like the back-propagation algorithm are often utilized, although they have drawbacks such as trapping of local minima, slower convergence, and compassion to log in. In this work, we presented a novel training strategy using a fuzzy firefly algorithm (FFA) for channel equalization. By using proper network topology and parameters, the suggested training system offers stronger exploitation and exploration skills, as well as the ability to solve the local minima issue. The performance of the equalizer can be analyzed by estimating two parameters i.e. MSE and BER. To exhibit the suggested technique's resilience in performance, the burst error situation was used, and the outcomes showed that the strategy is more effective in managing such situations than previous methods. The outcomes of the proposed method are presented through simulation, Furthermore, it proved that the suggested method validates a wide range of SNR, and also it outperforms the existing NN-based equalizers.

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Improved mutimodulus blind equalization algorithm for multi-level QAM signals with impulsive noise

Zhang

2023

Wireless Netw

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A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

Cited by 18 publications

References 26 publications

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Improved mutimodulus blind equalization algorithm for multi-level QAM signals with impulsive noise

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