Constant Modulus Blind Equalization Algorithm Research Articles

A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System

This paper proposes a wireless channel equalization method applied to the constant modulus blind equalization system, which addresses the slow convergence and strong randomness in the initialization of equalizer weights in the constant modulus blind equalization algorithm (CMA) by introducing a hybrid arithmetic whale optimization algorithm (HAWOA). The mean square error in the CMA is utilized as the cost function for the HAWOA to obtain a more effective initial weights for the equalizer. To validate the superiority of the hybrid arithmetic whale constant modulus blind equalization algorithm, tests are conducted on the equalization system using 16QAM and 64QAM signals. The simulation results demonstrate that the proposed algorithm achieved better initial weights compared to the CMA and the constant modulus blind equalization algorithm based on the whale optimization algorithm. It can obtain the desired mean square error with a lower symbol error rate in fewer iterations. Furthermore, the hybrid arithmetic whale constant modulus blind equalization algorithm exhibited faster convergence in optimizing initial weights, effectively enhancing the equalization performance of the CMA in wireless channels while ensuring timeliness.

Network Intrusion Feature Map Node Equalization Algorithm Based on Modified Variable Step-Size Constant Modulus

When the network is subject to intrusion and attack, the node output channel equalization will be affected, resulting in bit error and distortion in the output of network transmission symbols. In order to improve the anti-attack ability and equalization of network node, a network intrusion feature map node equalization algorithm based on modified variable step-size constant modulus blind equalization algorithm (MISO-VSS-MCMA) is proposed. In this algorithm, the node transmission channel model after network intrusion is constructed, and sequential processing is performed to intruded nodes with the variable structure feedback link control method. With diversity spread spectrum technology, the channel loss after network intrusion is compensated and the network intrusion map feature is extracted. According to the extracted feature amount, channel equalization processing is performed for the cost function with the MISO-VSS-MCMA method to reduce the damage of network intrusion to the channel. Simulation results show that in node transmission channel equalization after network intrusion, this algorithm can reduce the error bit rate of signal transmission in network, and provide a good ability of correcting phase deflection in the output constellation, thus avoiding the error bit distortion and channel damage caused by network intrusion to the signal with a good equalization effect. This algorithm provides stronger convergence and map concentration, which demonstrates that its anti-interference and signal recovery capabilities are better, so it improves the anti-attack ability of the network.

An Adaptive Momentum CMA Blind Equalization Based on Error Energy

To solve the problem of large steady state residual error of momentum constant modulus algorithm (CMA) blind equalization, a momentum CMA blind equalization controlled by energy steady state was proposed. The energy of the equalizer weights is estimated during the updating process. According to the adaptive filtering theory, the energy of the equalizer weights reaches to the steady state after the algorithm is converged, and then the momentum can be set to 0 when the energy change rate is less than the threshold, which can avoid the additional gradient noise caused by momentum and further improve the convergence precision of the algorithm. The proposed algorithm takes advantage of momentum to quicken the convergence rate and to avoid the local minimum in the cost function to some extent; meanwhile, it has the same convergence precision with CMA. Computer simulation results show that, compared with CMA, momentum CMA (MCMA) and adaptive momentum CMA (AMCMA) blind equalization, the proposed algorithm has the fastest convergence rate and the same steady state residual error with CMA.

DNA genetic artificial fish swarm constant modulus blind equalization algorithm and its application in medical image processing.

This study proposes use of the DNA genetic artificial fish swarm constant modulus blind equalization algorithm (DNA-G-AFS-CMBEA) to overcome the local convergence of the CMBEA. In this proposed algorithm, after the fusion of the fast convergence of the AFS algorithm and the global search capability of the DNA-G algorithm to drastically optimize the position vector of the artificial fish, the global optimal position vector is obtained and used as the initial optimal weight vector of the CMBEA. The result of application of this improved method in medical image processing demonstrates that the proposed algorithm outperforms the CMBEA and the AFS-CMBEA in removing the noise in a medical image and improving the peak signal to noise ratio.

An Optimized Combination of Frequency Domain Adaptive Equalizers

In order to overcome the defects of the high computational loads and selecting the threshold of mean square error (MSE) for time domain decision-directed constant modulus blind equalization algorithm (DD+CMA), a frequency domain parallel decision multi-modulus blind equalization algorithm based on frequency domain MMA(FMMA) and frequency domain LMS (FLMS) algorithm is proposed. The proposed algorithm is composed of the FMMA and FLMS, and the FMMA and FLMS run automatically in soft switching parallel manner. In running process, it is not necessary to selecting the threshold of the MSE. Moreover, the computational loads can be reduced by circular convolution in the frequency domain signals instead of linear one of the time domain signals. Simulation results show that performance of the proposed algorithm outperforms the FLMS and the FMMA algorithm.

Wavelet Frequency Domain Constant Modulus Blind Equalization Algorithm Based on Fractional Lower Order Statistics

In order to improve the performance of constant modulus algorithm(CMA) in the noise which obeys α-stable distribution, wavelet frequency domain constant modulus blind equalization algorithm based on fractional lower order statistics(WT-FLOSFCMA) is proposed. This proposed algorithm uses fractional lower order statistics to restrain α-stable noise, and its computational loads can be greatly reduced by using FFT technique and the overlapping retention law. In the proposed algorithm, according to the minimum dispersion coefficient criterion, the CMA is optimized, orthogonal wavelet transform is used to improve the convergence rate. The computer simulations in underwater acoustic channels show that the proposed algorithm outperforms than CMA, orthogonal wavelet transform constant modulus algorithm(WT-CMA),and frequency domain blind equalization algorithm based on fractional lower order statistics(FLOSFCMA) in suppressing with inter-symbol interfere, improving convergence rate, and reducing mean square error.

Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Artificial Fish Swarm Algorithm

According to disadvantages of big steady-state error, low convergence rate, and local convergence of traditional Constant Modulus blind equalization Algorithm (CMA), an orthogonal Wavelet Transform blind equalization Algorithm based on the optimization of Artificial Fish Swarm Algorithm(AFSA-WT-CMA) is proposed. In this proposed algorithm, the weight vector of the blind equalizer is regarded as artificial fish, the equalizer weight vector can be optimized via making full use of global search and information sharing mechanism of artificial fish school algorithm, the de-correlation ability of normalizing orthogonal wavelet transform. The computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms CMA and WT-CMA in convergence rate and mean square error.

Blind Equalization Algorithm Based on the Orthogonal Wavelet Transform for SIMO Systems

In order to improve the equalization effects of the constant modulus blind equalization algorithm (CMA) for Single-Input and Multiple-Output (SIMO) systems, orthogonal wavelet transform constant modulus algorithm (WT-CMA) based on SIMO is proposed. This proposed algorithm uses the orthogonal wavelet transform to decrease the autocorrelation of the input signals to accelerate the convergence rate and reduce the steady-state error. Theoretical analysis and computer simulations shows that the proposed algorithm has better performance and smaller steady-state error in SIMO systems, it is very easy to achieve in engineering.

Research on image transmission equalized by dual‐mode blind algorithm

ABSTRACTThe traditional equalization technologies expose some shortages while people put forward higher demand to quality of communication system and transmission rate. Single‐carrier frequency‐domain equalization (SC‐FDE) is becoming a research hotspot in wideband wireless communication area because of its special predominance. In recent years, the research on SC‐FDE develops quickly. We take a study on feasibility of building SC‐FDE system by comparing performance with orthogonal frequency division multiplexing. This paper takes a study on a dual‐mode direct decision‐least mean square+modify constant modulus algorithm blind equalization algorithm applied in a single‐carrier system. And then, we build the simulation model of image transmission in SC‐FDE system and get the direct and better result through the comparative analysis of image. Copyright © 2012 John Wiley & Sons, Ltd.

Orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on quantum particle swarm optimization

When constant modulus blind equalization algorithm (CMA) is used to equalize high-order QAM signals, there occur the defects of the slow convergence rate and big steady mean square error. In order to overcome these disadvantages, orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on the quantum particle swarm optimization (QPSO-WTWMMA) is proposed. In this proposed algorithm, quantum particle swarm optimization algorithm and orthogonal wavelet transform are combined into weighted multi-modulus blind equalization algorithm (WMMA) according to the feature of higher-order QAM signal constellations. Accordingly, the equalizer weight vector can be optimized by QPSO algorithm, the autocorrelation of the input signals can be reduced via using orthogonal wavelet transform, and WMMA is used to choose appropriate error models to match QAM constellations. The theoretical analyses and the computer simulations in underwater acoustic channels indicate that the proposed algorithm can obtain the fastest convergence rate and the smallest steady mean square error in equalizing high-order QAM signals. So, the proposed algorithm has important reference value for the underwater acoustic communications.

A Variable Step Size Blind Equalization Algorithm Based on Gamma Distribution

A variable step size Constant Modulus Algorithm (CMA) based on the gamma distribution is implemented as solutions to optimize the problem of blind equalization. The factor of step size in blind equalization algorithm is varied with gamma variable, in terms of the characteristics of which, the algorithm can search for the globe optimal equalizer weight vector. Simulation results indicate that the convergence rate and the steady Mean Square Errors (MSE) performances of the algorithm proposed are much better than conventional CMA and modified CMA blind equalization algorithms.

A Variable Step Size Blind Equalization Algorithm Based on Gamma Distribution

A variable step size Constant Modulus Algorithm (CMA) based on the gamma distribution is implemented as solutions to optimize the problem of blind equalization. The factor of step size in blind equalization algorithm is varied with gamma variable, in terms of the characteristics of which, the algorithm can search for the globe optimal equalizer weight vector. Simulation results indicate that the convergence rate and the steady Mean Square Errors (MSE) performances of the algorithm proposed are much better than conventional CMA and modified CMA blind equalization algorithms.

Variable Step-Size Wavelet Vector Machine Blind Equalization Algorithm

In order to overcome the contradiction of the CMA with a constant step-size between the convergence rate and the residual mean square error(MSE), on the basis of analyzing the idea of variable step-size, the feature of Support Vector Machine(SVM) and Wavelet Transform, a Variable step-size Wavelet transform Support vector machine Constant Modulus blind equalization Algorithm (VWSCMA) is proposed. In the proposed algorithm, the variable step-size is used to solve the contradiction between the convergence rate and the residual MSE, SVM is employed to optimize the weight vector of equalizer, and wavelet transform is used to reduce the autocorrelation of input signals of equalizer. Simulation results show that the proposed algorithm can effectively overcome the contradiction between the convergence rate and the residual error and has good equalization performance.

Wavelet Vector Machines Blind Equalization Algorithm Based on Variable Segmentation Error Function

To overcome the disadvantage of constant modulus algorithm's slow convergence and local minimum, this paper presents a wavelet vector machine blind equalization algorithm based on variable segmentation error function. This proposed algorithm uses support vector machine to optimize the initial weight vector, then, it switches to Wavelet Constant Modulus blind equalization Algorithm(WCMA) with odd symmetry variable segmentation error function. The computer simulation with underwater acoustic channel demonstrates that the proposed algorithm has fast convergence rate and small mean square error.

Blind Equalization Algorithm Based on Adaptive Genetic Algorithm and Wavelet Transform

Constant Modulus Algorithm(CMA) has slow convergence speed and easily immerges in local minimum owing to lack of initialization theory. Aiming at these disadvantages, adaptive genetic parameters are introduced into constant modulus blind equalization algorithm based on Genetic Algorithm and Wavelet Transform(GAWT-CMA), and Constant Modulus blind equali- zation Algorithm based on Adaptive Genetic Algorithm and Wavelet Transform(AGAWT-CMA) was proposed. The proposed algorithm processes genetic parameters adaptively, which can not only save the excellence individual with large probability but also avoid stagnancy during the evolution process. So it is propitious to search the whole optimum solution in overall range. The performance of the proposed algorithm was verified by computer simulation with underwater acoustic channels.

An Adaptive Constant Modulus Blind Equalization Algorithm and Its Stochastic Stability Analysis

A constant modulus algorithm is presented for blind equalization of complex-valued communication channels. The proposed algorithm is obtained by solving a novel deterministic optimization criterion which comprises the minimization of a priori as well as a posteriori dispersion error, leading to an update equation having a particular zero-memory continuous Bussgang-type nonlinearity. We also derive a stochastic bound for the range of step-sizes for a generic Bussgang-type constant modulus algorithm. The theoretical result is validated through computer simulations.

Two-stage interference-resistant adaptive periodically time-varying CMA blind equalization

We consider the problem of blindly equalizing a digital communication signal distorted by a linear time-invariant channel and contaminated by severe co-channel or adjacent-channel digital interference under the assumption that the latter exhibits a different symbol rate from the desired signal. The proposed equalizer is composed of two stages that are both periodically time-varying (PTV) in order to better match the periodical statistics of the received signal. The first stage employs linear PTV filtering to mitigate interference, thus allowing the second stage, based on the constant modulus algorithm (CMA), to reliably recover the transmitted information symbols. Computer simulations confirm the effectiveness of the new approach, and comparisons with existing blind methods show that a significant performance gain can be attained.

Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information

Constrained Cramer-Rao bounds are developed for convolutive multi-input multi-output (MIMO) channel and source estimation in additive Gaussian noise. Properties of the MIMO Fisher information matrix (FIM) are studied, and we develop the maximum rank of the unconstrained FIM and provide necessary conditions for the FIM to achieve full rank. Equality constraints on channel and signal parameters provide a means to study the potential value of side information, such as training symbols (semi-blind case), constant modulus (CM) sources, or known channels. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating the performance of SIMO and MIMO channel estimation and equalization algorithms. We present examples using the constant modulus blind equalization algorithm. The constrained bounds are also useful for evaluating the relative value of different types of side information, and we present examples comparing semi-blind, constant modulus, and known channel constraints. While the examples presented are primarily in the communications context, the CRB framework applies generally to convolutive source separation problems.

A vector constant modulus algorithm for shaped constellation equalization

The constant modulus algorithm (CMA), a popular method for performing blind equalization, has the drawback of failing when the input data has a Gaussian distribution or a kurtosis larger than three. This means that source shaping (which is used to increase the shaping gain) can produce data unequalizable by CMA and other important blind equalization techniques. This paper proposes a simple extension of CMA that blindly equalizes many types of shaped constellations, including those shaped by shell mapping.