Wavelet weighted multi-modulus blind equalization method based on simulated annealing wolf pack optimization

Abstract

The invention discloses a wavelet weighted multi-modulus blind equalization method based on simulated annealing wolf pack optimization. The method specifically comprises the following steps that a simulated annealing optimization method with stronger local search ability is embedded into a wolf pack optimization method with strong global optimization ability to obtain a simulated annealing wolf pack optimization method SA-MA, wherein the new method is used for minimizing a non-convexity cost function of a weighted multi-modulus blind equalization method WMMA; a fitness function of the SA-MA is determined through the cost function of the WMMA; an input signal of an equalizer is taken as an input of the SA-MA; and a finally-obtained position vector for monkeys is taken as an initial weight vector of the WMMA, the cost function obtains a minimum value at this moment, a blind equalization system becomes an expected ideal system, and a signal-to-noise ratio is reduced by utilization of wavelet transform, so that a good equalization effect of a high-order QAM signal is obtained. Compared with the similar technology, when the high-order QAM signal is equalized, the method has faster convergence speed, smaller steady state error, better equalization effect and strong practical value.

Description

Technical field: [0001] The invention belongs to the technical field of signal processing, specifically a wavelet weighted multi-mode blind equalization method based on simulated annealing wolf pack optimization. Background technique: [0002] At present, underwater acoustic communication is a generally accepted underwater communication method. Factors such as multipath propagation and high background noise in underwater acoustic channels will cause serious inter-symbol interference (ISI) during signal transmission. , the communication quality cannot be guaranteed. To solve this problem, various equalization techniques have emerged. The weighted multi-mode blind equalization method (WMMA) uses the exponential power of the decision symbol to form a weighted item, which can adaptively modulate the modulus value and effectively reduce the model error. However, when obtaining the global optimal solution of the non-convex cost function of WMMA, it still uses Without the idea of...

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Problems solved by technology

The weighted multi-mode blind equalization method (WMMA) uses the exponential power of the decision symbol to form a weighted item, which can adaptively modulate the modulus value and effectively reduce the model error. However, when obtaining the global optimal solution of the non-convex cost function of WMMA, it still uses The idea of ​​gradient descent cannot overcome the problem that it is easy to fall into local extremum, and it is difficult to further improve the balance effect

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