Orthogonal Wavelet Transform Blind Equalization Algorithm Based On The Optimization Of Ant Colony Algorithm

The severe inter-symbol interference, caused by multi-path effects and channel distortion of sound propagation, degrades the reliability and decreases the rate of the propagation, is the chief obstacle of high speed underwater sound correspondence. At present, it is a very significant topic to research on channel blind equalization technology and it is also an advanced topic in modern signal processing field which does not need to transmit the training sequence to be possible to track the channel change, and enhances the data effectively the transmission speed and the reliability. Blind equalization operating can equalize channels using statistic characteristic of received signal to reduce or eliminate ISI, so utilization quotiety of bandwidth is improved greatly. With the rapid development of wavelet transform theory, a new way is supplied for transform domain blind equalization algorithm study. The article integrates ant colony optimization algorithm with WT-CMA,searched the equalizer weight vector of the global optimal solution quickly and initialized the weight vector of equalizer, the performance of the proposed algorithm can be improved via full using the global search and positive feedback mechanism of ant colony optimization algorithm, the de-correlation ability of wavelet transform.. The article has conducted the thorough research to orthogonal wavelet transform blind equalization algorithm based on the optimization of ant colony algorithm.The main contributions are as follows:1 Analyzing blind equalization based on ant colony algorithmThe proposed algorithm integrates ant colony optimization algorithm with blind equalization, quickly searched the global optimal solution by means of the group of candidate solutions for the evolution and initialized the weight vector of equalizer the performance of the proposed algorithm can be improved via full using the global search and positive feedback mechanism of ant colony optimization algorithm, blind equalization based on ant colony algorithm are put forward. Aiming at the contradiction of constant modulus algorithm (CMA) with a constant step-size between the convergence rate and the residual error, the variable step-size theory is applied to CMA. So, the variable step-size CMA based on ant colony algorithm are put forward. The efficiency of the algorithm was proved by computer simulations. 2 Analyzing orthogonal wavelet transform blind equalization algorithm based on the optimization of ant colony algorithmAfter analyzing the factors affected the performance of blind equalization, we knowed that the equalizer input signal's autocorrelation is one of the important factors which affect the convergence rate. The autocorrelation of signal various components can be effictively reduced after orthogonal wavelet transform (WT) and being normalizated the energy in wavelet transform domian, and the convergence rate of equalization algorithm can be speeded up effectively. Hence, orthogonal wavelet transform blind equalization algorithm based on the optimization of ant colony algorithm (ACO-WT-CMA) and orthogonal wavelet transform super-exponential iterative blind equalization algorithm based on the optimization of ant colony algorithm (ACO-WT-SEI) are proposed, via analyzing the bases of the performance of WT-CMA. The simulation results show that a relatively low mean square error and fast convergence rate has been achieved.3 Analyzing orthogonal wavelet packet transform blind equalization algorithm based on the optimization of ant colony algorithmComparing with orthogonal wavelet transform, the autocorrelation of input signal is futher reduced via the signal scale space and wavelet space are decomposed respectively by orthogonal wavelet packet transform(WPT). Hence, orthogonal wavelet packet transform blind equalization algorithm based on the optimization of ant colony algorithm(ACO-WPT) is proposed, after combining WPT with the optimization of ant colony algorithm(ACO). Theoretic analysis and computer simulations demonstrate that the presented algorithm has fast convergence rates, small steady-state error and good noise immunity.