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.

Indexing terms:

• Blind equalization
• Neural networks
• Non linear function

Additional information

Notes on contributors

Rajoo Pandey

Rajoo Pandey, was born In Chhatarpur, Madhya Pradesh on 5th October, 1968. He received his BE degree in Electronics & Communication Engineering from Government Engineering College, Jabalpur and MTech from Regional Engineering College, Kurukshetra in 1989 and 1991 respectively. In 1991 he joined as a lecturer in the department of Electronics and Communication Engineering, REC Kurukshetra. He was on studyleave from 1998 to 2001 for the PhD program from NT Roorkee. His fields of interest include, signal processing, communication systems and neural networks.