Adaptive equalization using complex-valued multilayered neural network based on the extended Kalman filter

In this paper, a complex-valued neural network based on the Kalman filter is presented for channel equalization in a communication system. The complex-valued decoupled extended Kalman filter algorithm is derived which provides a faster convergence rate and better performance than those of complex back-propagation algorithms. Computer simulation results showed that the proposed scheme has a faster convergence behavior and smaller signal constellation than the gradient descent based learning algorithm via the linear and nonlinear channel equalization problem