Simulated Annealing and Drosophila Hybrid Optimal Wavelet Generalized Discrete Multimodal Blind Equalization Method

Abstract

The invention discloses a simulation annealing and fruit fly hybrid optimization wavelet generalized discrete multi-module blind equalization method. The method comprises initializing location vectors of fruit flies in a swarm of fruit flies to serve as the decision variable of the simulation annealing method and the fruit fly hybrid optimization method, taking an input signal of an orthogonal wavelet transformer as the input of the hybrid optimization method, determining a smell concentration function of the fruit flies by a cost function of the generalized discrete multi-modulus blind equalization method, performing simulation annealing operation on the optimal location vector of the swarm of the fruit flies obtained through the fruit fly optimization method, obtaining the global optimal location vector, which does not fall into a local minimum, of the swarm of the fruit flies, and taking the location vector as the initialization weight vector of the wavelet generalized discrete multi-modulus blind equalization method. The method, while processing high-order orthogonal amplitude modulation signals, is rapid in convergence, small in steady state error, overcomes a defect of falling into the local optimum, and has strong practicality.

Description

Technical field [0001] The invention relates to a simulated annealing and Drosophila hybrid optimization wavelet generalized discrete multi-mode blind equalization method. Background technique [0002] Traditional Multi-modulus Algorithm (MMA) (see reference [1] Xu Xiaodong, Dai Peichu, Xu Xiaoxia. Weighted multi-mode blind equalization algorithm suitable for high-order QAM signals[J].Journal of Electronics and Information Technology,2007.6,29( 6):雨1352~1355. Literature [2]J.Yang,GDumont.TheMultimodulusBlindequalizationandItsGeneralizedAlgorithms.2002(20)5:997-1015.) The real part and imaginary part of the QAM signal are balanced, which effectively corrects the QAM signal Phase rotation, but for high-order QAM signals, the MMA equalization effect is still not ideal. [0003] The generalized discrete multi-mode blind equalization method uses a gradient descent algorithm to update the equalizer weight vector, which is easy to fall into local convergence and difficult to obtain the g...

AI Technical Summary

Problems solved by technology

[0005] The simulated annealing method simulates the solid annealing process, adopts a serial optimization structure, and endows the search process with a time-varying probability that eventually tends to zero, thereby effectively avoiding falling into the local extremum and eventually tending to the global optimum. Excellent local search ability, but its slow convergence speed, weak global search ability, and poor initial value robustness (see literature [7] Pang Feng. The principle of simulated annealing algorithm and its application in optimization problems [D]. Jilin: Jilin University. 2006:6-8. Literature [8] Song Wei, Liu Qiang. Research on Process Mining Based on Simulated Annealing Algorithm [J]. Electronic Journal, 2008,36(4A):35-139)

Images