A Wavelet Norm Blind Equalization Method Based on Adaptive Step Size Monkey Swarm Optimization

Abstract

The invention discloses a wavelet norm blind equalization method based on adaptive step length monkey group optimization. An adaptive step length is imported in a monkey group optimization method to obtain an adaptive step length monkey group optimization method, the new method has very good global optimization ability, an optimal position vector of a monkey group in a search space is obtained by the method, the vector is used as an initial vector in the norm blind equalization method based on wavelet transform, so that a blind equalization system is closer to an expected ideal system, accordingly the convergence speed is accelerated, and the mean square error is reduced. Compared with the prior art, the wavelet norm blind equalization method disclosed by the invention has the advantages of few parameters, low complexity, high convergence speed, small steady state error and good practical value.

Description

Technical field: [0001] The invention relates to the technical field of data processing, in particular to a wavelet norm blind equalization method based on adaptive step length monkey group optimization. Background technique: [0002] When the signal is transmitted at high speed in the underwater acoustic digital system, Inter-Symbol interference (ISI) will occur due to factors such as limited bandwidth and multipath propagation, resulting in serious distortion. If the blind equalization technology is introduced at the receiving end, it can Effectively eliminate and reduce ISI, improve communication quality. The constant modulus algorithm (Constant Modulus Algorithm, CMA) has a simple structure and stable performance, and is currently widely used, but it has problems such as slow convergence speed and large steady-state error; the wavelet transform (WT) is introduced into CMA, and the signal can be reduced by using WT. Autocorrelation with noise, although WT-CMA has improve...

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Problems solved by technology

The constant modulus algorithm (Constant Modulus Algorithm, CMA) has a simple structure and stable performance, and is currently widely used, but it has problems such as slow convergence speed and large steady-state error; the wavelet transform (WT) is introduced into CMA, and the signal can be reduced by using WT. Autocorrelation with noise, although WT-CMA has improved performance compared with traditional CMA, the cost function in the method is still multi-modal, and the optimization method still uses the gradient idea of ​​CMA, so it cannot be obtained from Fundamentally solve the problem that the global optimal solution cannot be obtained, and the effect is limited

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