A Study Of Equalization Algorithm With Constant Modulus Using Momentum Feed-Forward Neural Networks

Blind equalization is an adaptive equalization technique without a training sequence; it can equalize the properties of the channel by using the statistic properties of the received signals and overcome the disadvantages of conventional adaptive equalization techniques. Thus, it has become a major focus in the field of digital communication.The artificial neural network is a theoretical mathematical model for the cerebral neural networks; it is an information-processing system that is based on stimulating the structure and function of the cerebral neural networks; it can perform complicated operations and establish nonlinear relationships. The use of neural networks for designing equalizer is of important theoretical significance and practical value.The primary subject of this paper is to(1)Evaluate the disadvantage of conventional back-propagation (BP) algorithms, discuss their improved forms, and propose a blind equalization algorithm using momentum feed-forward neural networks. Computer stimulation shows that the convergence speed of this new algorithm is somewhat increased as compared with BP algorithm.(2) Investigate the effects on the function of algorithm of the momentum in the new algorithm and of the learning rate, and propose two blind equalization algorithms using varying momentum feed-forward...