Study On Adaptive Equalization Algorithms Based On Wavelets And Neural Networks

Adaptive equalization is one of the most important technique in modern communications. So, studying how to improve the convergence speed and symbol error performance of adaptive equalization has important theoretical significance and practical value. In this dissertation, we focus on wavelet and neural network equalization techniques in the presence of additive noise, linear and nonlinear distortion. The main research work and results are as follows:1. A new nonlinear adaptive equalizer in wavelet transform domain is proposed, which is constructed by combining wavelet transform with the connectionist model. Since the correlation matrix of a signal in wavelet domain has sparse structure, the convergence speed of equalizer can be improved by using this good decorrelation ability of wavelet transform. Based on this, the corresponding equalization algorithm is given. Also its characters are analyzed, and the simulation results show its convergence speed is faster and its computation complexity is smaller.2. By the analysis of wavelet packets, a new wavelet packet transformed equalization algorithm is presented. Since wavelet packet transform has a better decorrelation ability than wavelet transform and it is combined into the connectionist model,the convergence speed of new equalization algorithm is improved further by normalization least mean square algorithm in wavelet packet transform domain. 3. For nonlinear channel with severe linear distortion and mild nonlinear distortion, two decision feedback equalizers employing wavelet transform and wavelet packet transform are presented respectively. The corresponding adaptive equalization algorithms are also investigated. So by using the decision feedback construction and wavelet transform, convergence speed and symbol error performance are both improved further more.4. A new equalizer represented by a set of orthogonal multiwavelet is presented. Since the multiwavelet transformed correlation matrices have smaller boundary effects than that of wavelet, a new multiwavelet transform domain Newton-LMS adaptive equalization algorithm is described. Its convergence speed is fast and its complexity is only by using the preconditioned conjugate gradient algorithm.5. Several new kinds of complex equalizers applying to QAM-signals are presented. The structures of them are constructed by complex radial basis function neural networks (RBFNs) and the algorithms for them are also given. Theoretical analysis and the experimental results show that the new algorithms have better convergence propertythan RBFNs-based equalizer available.6. For several typical wavelet networks, their applications to equalization are investigated, which show that the performance of equalizer based on one-dimensional wavelet networks is relatively poor. For multidimensional wavelet network-ed equalizer, a method to avoid 'curse of dimensionality' and a new equalizer are proposed. By the analysis of the inverse model of nonlinear channel, a new method of wavelet neural networks to approximate the inverse of nonlinear channel is presented. Then, a new wavelet neural network equalizer and its adaptive algorithm used for nonlinear channel with mild nonlinear distortion are also given.7. The concept and the model of multiresolution orthogonal multiwavelet neural network with hierarchical structure and localized learning are proposed. Its properties for function approximation and algorithm are also investigated. The theoretical analyses show that orthogonal multiwavelet neural networks have more rapid convergence speed than orthogonal wavelet networks. To make a comparison between both networks, experiments for several nonlinear functions are carried out with GHM multiwavelet orthogonal networks and Daubechies 2 wavelet orthogonal networks.