Adaptive Channel Equalization using Multilayer Perceptron Neural Networks with variable learning rate parameter

Chemweno, Emmanuel K.

doi:10.9790/4200-0702013440

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Numerous neural network architectures were considered for blind equalization. We can mention, the feedforward equalizer (FFE) [12], the feedforward with decision feedback equalizer (FFE-DF) [15], the recurrent neural network equalizer (RNNE) [16] and variational autoencoders [17]. All these previous architectures process the real and imaginary parts of the signal separately, thus ignoring the correlation between the real and imaginary parts.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

Farhati,

Feki,

Shaiek

et al. 2023

IEEE Access

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One of the major problems faced in digital communication systems is Inter-Symbol Interference (ISI), induced by the propagation channel in the single carrier based systems. Classic digital equalization techniques based on pilot training sequences become tedious in the presence of nonlinear power amplifiers. The existing techniques for digital pre-distortion need a high transmitter computation complexity and those for the post-distortion require pilot overhead. In this review, we focus on fully generic blind processing for both channel equalization and power amplifier post-distortion. We propose a receiver based on two complex-valued neural networks (CV-NN). The first CV-NN is dedicated to generic blind equalization (GBE) to mitigate ISI. The second one is used for generic blind post-distortion compensation (GBPDC) of the power amplifier nonlinearity (PANL). The GBE and the GBPDC have no prior information about the transmission channel, the used constellation, and the PA model. For the first CV-NN, we consider an updated probability density fitting (PDF) based criteria, corresponding to many assumed possible constellations, that are used jointly with an automatic modulation classification (AMC) based on the k-nearest neighbors (KNN) algorithm. For the second CV-NN, we use the final updated PDF criterion resulting from the first CV-NN training process. Numerical results show that our generic blind Deep learning-based signal receiver formed with the two CV-NNs is effective in alleviating the two coupled signal distortions: the ISI and the PANL. Compared with the state-of-the-art methods, banking on a supervised post-distortion compensation and channel equalization, the proposed generic blind DL-based scheme exhibits good detection performance. INDEX TERMS automatic modulation classification; blind channel equalization; neural network; postdistortion; power amplifier; pre-distortion; probability density fitting.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

Farhati,

Feki,

Shaiek

et al. 2023

IEEE Access

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“…Several neural network architectures were used for blind equalization. We can mention for examples, the multilayer perceptron (MLP) or feedforward equalizer (FFE) [3], the feedforward with decision feedback equalizer (FFE-DF) [7], the recurrent neural network equalizer (RNNE) [8] [9] and variational autoencoders [10] [11]. Since the equalized signal is complex and to reduce the implementation complexity, in this paper, we have implemented a complex valued feed forward architecture.…”

Section: Introductionmentioning

confidence: 99%

Blind channel equalization based on Complex-valued neural network and probability density fitting

Farhati¹,

Fki²,

Bey³

et al. 2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Performance Comparison of Neural Network based Equalizers for Wireless Communication Channels

Ghadei

Sahoo

2020

2020 IEEE Applied Signal Processing Conference (ASPCON)

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Adaptive Channel Equalization using Multilayer Perceptron Neural Networks with variable learning rate parameter

Abstract: This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN

Cited by 3 publications

References 4 publications

Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

Blind channel equalization based on Complex-valued neural network and probability density fitting

Performance Comparison of Neural Network based Equalizers for Wireless Communication Channels

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