Blind Equalization of Nonlinear Communication Channels using Modified FLANN

Abstract

Adaptive blind equalization has gained widespread use in communication systems that operate without training signals. For the nonlinear channels, however, the linear equalizers are not suitable. Nonlinear mapping capability of neural networks makes them a suitable choice for the equalization of nonlinear channels. In this paper, the application of modified Functional Link Artificial Neural Network (FLANN) with adaptable output node alongwith its learning rule for the blind equalization of nonlinear communication channels is presented. This modification in the FLANN helps in achieving faster convergence. The performance of the proposed network is compared with that of Radial Basis Function (RBF) blind equalizer and the linear Constant Modulus Algorithm (CMA). The small size and simple learning rules make this network suitable for high speed blind equalization.