Orthogonal Wavelet Transform Blind Equalization Algorithm Simulation Based On The Optimization Of Particle Swarm

At present, the researth of combined with the three algorithm is very few, particle swarm optimization algorithm, the wavelet transformation theory, the blind equalization algorithm, at home and abroad.But it is a great significance to research on communication signal processing by combing the three algorithm. This article studies the use of PSO and equalizer weight vector in improved PSO optimize blind equalizer, and use the wavelet transformation theory to reduce the autocorrelation of the input signals, improved balance performance in blind equalization algorithm.The main contributions are as follows:1Analyzing wavelet transform blind equalization algorithm based on the optimization of particle swarm algorithmThe equalizer initialization vector can be found quickly by particle swarm algorithm, avoid producing local limit that caused by the gradient descent method to find the most optimal vector.The computer simulation results show that, through the proper adjustment of the parameters of the particle swarm optimization algorithm, the proposed algorithm can get better superiority than constant modulus algorithm and orthogonal wavelet blind equalization algorithm.2Analyzing wavelet transform blind equalization algorithm based on the optimization of Immune Clone Particle SwarmThe introduction of immune cloning algorithm in Particle swarm algorithm, in order to increase population diversity in PSO,and overcoming the phenomenon of local extreme value point which caused by poor diversity in later evolution of particle swarm algorithm, also avoid the premature convergence and enhance the global optimization ability of particle swarm optimization algorithm. The effectiveness of the proposed algorithm was verified in the experiment of underwater acoustic channel simulation.3Analyzing wavelet transform weighted multi-modulus blind equalization algorithm based on the optimization of quantum particle swarm(1) In order to solve the problem of low convergence rate and big steady-state error in fractionally spaced blind equalization algorithm, an orthogonal wavelet transform fractionally spaced constant modulus blind equalization algorithm based on the optimization of quantum particle swarm, via the analyzing of the futures of fractionally-spaced Equalizer input signals for orthogonal Wavelet transform, equalizer weight vector can be optimized by quantum particle swarm algorithm. Accordingly, So as to reduce steady mean square error and improve convergence rate. The proposed algorithm can better improve the equilibrium properties of FSE-CMA.(2) For the problem of Multi-Modulus blind equalization Algorithm (MMA) in used to equalize high-order QAM signals, it has 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 optimization of quantum particle swarm (QPSO-WTWMMA) is proposed. In this proposed algorithm, quantum particle swarm optimization algorithm and orthogonal wavelet transform are used into Weighted Multi-Modulus blind equalization algorithm (WMMA) according to the feature of high-order QAM signal constellations. Accordingly, the equalizer weight vector can be optimized by QPSO. Orthogonal wavelet transform is used to reduce the autocorrelation of the input signals and used to choose appropriate error model to match QAM constellation. The computer simulations in underwater acoustic channels indicate that the proposed algorithm can obtain faster convergence rate and lower steady mean square error.4Analyzing wavelet transform dynamic weighted multi-modulus blind equalization algorithm based on the Dynamic Particle Swarm OptimizationFor improving the equalization performance of high-order QAM signals, orthogonal Wavelet Transform Dynamic Weighted Multi-Modulus blind equalization Algorithm based on the Dynamic Particle Swarm Optimization (DPSO-WTDWMMA) is proposed. In this proposed algorithm, dynamic particle swarm optimization algorithm and orthogonal wavelet transform are used into Dynamic Weighted Multi-Modulus blind equalization Algorithm (DWMMA). Accordingly, the equalizer weight vector can be optimized by DPSO algorithm, the autocorrelation of the input signals can be reduced via using orthogonal wavelet transform, and DWMMA is used to choose appropriate error models to match QAM constellations. The theoretical analyses and 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 in the underwater acoustic communications.