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Author: natalia jiménez blind equalization of awgn for bpsk modulation.
Blind nonlinearity equalization by machine learning based clustering for single- and multi-channel coherent optical ofdm. Abstract—fiber-induced intra- and inter-channel nonlinearities are experimentally tackled using blind nonlinear equalization (nle) by unsupervised machine learning based clustering (mlc) in ~46-gb/s single-channel and ~20-gb/s (middle- channel) multi- channel coherent multi-carrier signals (ofdm- based).
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved either through random projections or by applying a fixed structured compression matrix, this paper.
Blind equalization in neural networks hardcover – december 18, 2017 by liyi zhang (author) the book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks.
A blind equalization scheme with a nonlinear structure that can form nonconvex decision regions is necessary. Multilayer feedforward neural networks provide a powerful device for approximating a nonlinear input-output mapping of a general nature. Many studies have shown that multilayer feedforward neural networks can form.
This letter considers the problem of blind equalization in digital communications by using linear neural network. Unlike most adaptive blind equalization methods which are based on matrix decomposition or the hankel property of matrix, we give a stochastic approximate learning algorithm for the neural network according to the property of the correlation matrices of the transmitted symbols. The network outputs provide an estimation of the source symbols, while the weight matrix of network.
Nov 11, 2020 research on the blind equalization technology based on the complex bp neural network with tunable activation functions.
To address this issue, we propose a novel blind equalization-aided deep learning (dl) approach to recognize qam signals in the presence of multipath propagation. The proposed approach consists of two modules: a blind equalization module and a subsequent dl network which employs the structure of resnet.
Abstract: in this chapter, the basic principle of feed-forward neural network (ffnn) is analyzed. First, blind equalization algorithms based on the three-layer ffnn, four-layer ffnn, and five-layer ffnn are studied.
The paper considers adaptive blind equalization problem of multichannel systems in digital communication. A feedforward neural network with lateral connections is introduced as the equalizer to estimate the source symbols from the received signals only. The lateral connections of the network are able to perform the self-orthogonalization, and thus the network can improved the performance.
Blind equalization in neural networks theory, algorithms and applications 1st edition by liyi zhang and publisher de gruyter. Save up to 80% by choosing the etextbook option for isbn: 9783110449679, 3110449676. The print version of this textbook is isbn: 9783110449624, 3110449625.
In this paper, we propose a novel blind equalization approach based on radial basis function (rbf) neural networks. By exploiting the short-term predictability of the system input, a rbf neural net is used to predict the inverse filter output.
The application of a radial basis function neural network (rbf) for blind channel equalization is analysed. This architecture is well suited for equalization of finite.
Fuzzy neural network blind equalization algorithm based on signal transformation. 作 者： guo, yecai liu, zhengxin; 作 者 单 位： school of electrical.
In the context of blind channel equalization, results have shown that the ptrbfnn not only solves the phase uncertainty of the classical complex valued rbfnn but also presents a faster convergence rate.
And we proposed feed-forward neural network blind equalization algorithm by research of traditional neural network blind equalization algorithm. And it is using a feed-forward neural network of the hidden layer to approximate the objective function.
Dec 14, 2018 we apply the techniques from meta-learning and machine learning to the communications domain.
For quadrature amplitude modulation (qam) signals, a new neural network online blind equalization algorithm based on the kalman filter (kf) is proposed.
Blind equalization is a digital signal processing technique in which the transmitted signal is inferred (equalized) from the received signal, while making use only.
Leung, blind equalization using a predictive radial basis function neural network, ieee trans.
Facebook is using similar technology to describe photos to blind people. The results are mixed, of course, but it's fascinating to watch the neural network make mistakes (and sometimes correct.
Among the useful blind equalization algorithms, stochastic-gradient iterative equalization schemes are based on minimizing a nonconvex and nonlinear cost function. However, as they use a linear fir filter with a convex decision region, their residual estimation error is high. In the paper, four nonlinear blind equalization schemes that employ a complex-valued multilayer perceptron instead of the linear filter are proposed and their learning algorithms are derived.
In this paper, we propose a fuzzy neural network blind equalization algorithm based on radial basis function (fnn-rbf-ble). The proposed algorithm defines the centers of rbf equalizer by analyzing the relationship of the input signal of equalizer and transmitted signal, therefore the structures of equalizer become simpler and the convergence speed become faster.
In this paper, a new blind equalization algorithm based on the t-s fuzzy neural network controller is proposed. Cma adopts fixed step-size, which affects the performance of algorithm. The t-s fuzzy neural network is introduced to adjust the step-size of blind equalization.
Keywords: neural network, equalizer, phase, concurrent, blind.
Abstract—this paper proposes neural networks-based turbo equalization (teq) applied to a non linear channel.
Jul 5, 2018 on neural networks are proposed to realize blind equalization and decoding process without the knowledge of channel state information (csi).
A neural network blind equalization algorithm is derived and used in conjunction with zigzag coding to restore the original image. As a result, the effect of psf can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (isi).
Blind equalization using a predictive radial basis function neural network abstract:.
Blind equalization in neural networks: theory, algorithms and applications kindle edition by liyi zhang (author), tsinghua university press (contributor) format: kindle edition the book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks.
Keywords—blind channel equalization; neural networks; multi-layer perceptron. Introduction in the recent years, mobility of communicators has added new challenges in the path to accomplish the goal of providing all the information asked for in any possible location. One of the new challenges is to conceive highly reliable and fast.
And non-blind equalization structures based on artificial neural. Networks (anns ) and keywords—blind channel equalization; neural networks.
A new approach to decision-directed(dd) blind equalization is introduced based on a neural network classification technique. The new ~dalgorithm, termed soft decision-directedequalization algorithm, is most effective for reconstructing psk and qpsk signals. It can also be extended to higher order qam signals at the expense of computational complexity.
In blind equalization problems, not all points can be obtained with different mapping functions. Therefore, neural networks find it difficult to solve blind equalization problems by themselves. This paper develops an approach to combine the advantages of higher order cumulants and neural network techniques to solve the problems of blind equalization.
A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (vaes), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new vae blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer.
The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.
The t-s fuzzy neural network is introduced to adjust the step-size of blind equalization. The structure and state functions of the t-s fuzzy neural network controller is provided in this paper. The cost function is proposed, and the iteration formulas of parameters are derived.
Blind equalization is a new adaptive technology in communication, signal, and information processing that uses the prior information of received signals to equalize the channel characteristics, so a training sequence in not needed.
Apr 27, 2020 jaakko lehtinen: self-supervised denoising using blind-spot convolutional networksmachine how convolutional neural networks work.
The efficacy of complex-valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies.
The blind equalizers based on complex valued feedforward neural networks, for linear and nonlinear communication channels, yield better performance as compared to linear equalizers. The learning algorithms are, generally, based on stochastic gradient descent, as they are simple to implement.
In this context, this paper proposes a new blind concurrent equalization approach that combines a phase transmittance radial basis function neural network (ptrbfnn) and the classic constant modulus algorithm (cma) in a concurrent architecture, with a fuzzy controller (fc) responsible for adapting the ptrbfnn and cma step sizes.
The use of neural nets to combine equalization with decoding for severe intersynbol interference channels.
Linear [17] and nonlinear (neural-networks-based) equalizers [18] use the kullback–leibler divergence between densities as a cost function.
1999b, “a fast and robust fixed-point algorithms for independent component analysis,” ieee trans.
Neural networks, has been pioneered by linsker (1992), becker and hin- ton (1992), atick and redlich (1993), plumbley and fallside (19881, and others. The second is the use, in signal processing, of higher-order statis- tics for separating out mixtures of independent sources (blind separation).
Blind equalization is a digital signal processing technique in which the transmitted signal is inferred from the received signal, while making use only of the transmitted signal statistics. Blind equalization is essentially blind deconvolution applied to digital communications.
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