Orthogonal Wavelet Norm Blind Equalization Method Based on Artificial Immune Network

Abstract

The invention discloses an orthogonal wavelet norm blind equalization method based on an artificial immune network, which includes the following steps: initialization: designing the initial antibody population; calculating the fitness value; cloning and variation: cloning and variation for each antibody; calculating Fitness value: For each clone, select the individual with the largest fitness value to form a new antibody population, and calculate the average fitness value of the population; compare the average fitness value of the population: if the average fitness value of this time is the same as the above If the value of the second iteration is different, go back to calculating the fitness value and perform the following operations; otherwise, continue to the next step; antibody suppression: calculate the affinity between antibodies, and compare the fitness of antibodies whose affinity is lower than the inhibition threshold, and eliminate the adaptation Antibodies with low density, the retained antibodies are used as memory cells of the network; diversity is introduced: if the iteration termination condition is not met, that is, the number of generations is cut off, antibodies are randomly generated and added to the original antibodies until the iteration is terminated.

Description

technical field [0001] The invention relates to an orthogonal wavelet normal mode blind equalization method based on an artificial immune network in underwater acoustic communication. Background technique [0002] In underwater acoustic communication, due to the influence of channel fading and multipath propagation, serious intersymbol interference will occur at the receiving end, which increases the bit error rate of the system. In order to overcome the intersymbol interference and improve the performance of the system, an equalization technique needs to be adopted at the receiving end. At present, the adaptive equalization technology based on the training sequence or the blind equalization technology without the training sequence are mainly used. Among the blind equalization methods, the constant modulus algorithm (Constant Modulus Algorithm, CMA) has a simple principle, a small amount of calculation, and robust performance. It can adapt to general digital communication s...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the traditional normal mode blind equalization method such as slow convergence speed, large mean square error, and easy to fall into local minimum points.

Images