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To solve the phase distortion and high error rate in optical signal transmission, an equalized technique is proposed, which aims to improve the constant modulus algorithm (CMA). In order to correct phase rotating and reduce the error rate with 64 quadrature amplitude modulation (QAM), the method takes the mean square error as the judgment and utilizes the time-varying step size. Simulation results demonstrate that the proposed algorithm can improve the convergence speed of constellation points, make the eye opening larger, and the signal noise ratio (SNR) can be increased by 4 dB under the same bit error rate (BER), which is efficient for the recovery of information in high-speed transmission.

The modern digital high speed wireless communication system demands quick convergence rate and low steady state error. The balancing between the demands can be achieved by opting step size. Thus, it is essential to define new algorithms to equalize channels and mitigate noise in communications. It is renowned that time varying step size blind equalization technique can speed up the convergence rate and minimize the misadjustment. This work presents a variable step size (VSS) approach based on godard blind equalization algorithm to resolve the conflict between the convergence rate and precision of the fixed step-size godard algorithm. The results of this projected approach is compared with the existing variable step size sato algorithm for a pulse amplitude modulated (PAM) input symbol.

In digital communications, blind equalization is an effective means for combating intersymbol interference (ISI) caused by multipath fading wireless channels, and its two main performance criterions are convergence rate and residual mean square error (MSE). The iterative form of super-exponential (SEI) blind equalization algorithm converges fast, and is convenient for compensating the distorted channels. Since real channels are generally non-minimum phase systems, linear equalizers can not perform well under this circumstance. Based on nonlinear structure, a novel fractionally-spaced super-exponential iterative decision-feedback blind equalization (FSSEIDFE) algorithm for deep frequency fading and severely nonlinear phase distortion channels is proposed, and fast convergence rate as well as low residual MSE is obtained. The simulation results prove the validity of the proposed method.

In this paper, a modified blind equalization algorithm based on the autocorrelation function of error signal has been proposed to enhance the performance of conventional constant modulus algorithm (CMA). In the modified algorithm the step-size is adjusted by the autocorrelation of error signal. Simulation results in the 16-QAM system show that the modified algorithm possesses faster convergence rate at early stages of iteration and smaller final misadjustment than CMA.

In this paper, a new blind equalization algorithm based on fuzzy neural network (FNN) is proposed. It makes use of blind estimation (BE) and FNN classifier to equalize. Firstly BE algorithm is used to identify the channel character, the signals are rebuilt by deconvolution, and then the signals are classified by FNN classifier. This algorithm has the merits than the foregoing neural network algorithm, such as faster convergence speed, smaller residual error, lower bit error rate (BER), etc. The validity is proved by simulations.

One of the main obstacles on reliable high-speed underwater acoustic communications through severely band-limited underwater acoustic channels is the intersymbol interference (ISI) caused by the multipath effect. Channel equalization technique is an effective way to combat ISI. One of the main disadvantages of the most commonly used constant modulus algorithm (CMA) is its slow convergence rate. In order to speed up the convergence rate, a new modified approach named decorrelation based constant modulus algorithm (DBCMA) for eliminate the underwater channel resulting multipath effect is proposed, and the computer simulation results confirm the effectiveness of the proposed algorithm.

In order to optimize high-order cumulant blind equalization algorithm, in this paper we propose an improved algorithm which can optimize the SW criterion with genetic algorithm and improve the algorithm performance with the global and fast convergence of genetic algorithm. Computer simulation results demonstrate that the improved algorithm has good convergence performance and error symbol resistance performance.

The blind equalization algorithm takes advantage of the prior information of the received signals to equalize channel, overcome the introduction of “training sequence” and improve the equalization efficiency. The paper puts forward blind equalization algorithm of constant norm applicable for high-order QAM signals, which takes advantage of the reuse technology of small sample to redesign weighting coefficient of equalizer to improve the convergence efficiency and reduces greatly the steady state error of system without making algorithm more complex. The paper further verified the effectiveness of algorithm through the comparison simulation analysis of blind equalization algorithm of underwater acoustic channel.