...::Mehran University Research Journal of Engineering & Technology::...

Article Information
Channel Equalization Using Multilayer Perceptron Networks Keywords: Blind Channel Equalization, Neural Networks, Noisy Signal, Multi Layer Perceptron, Error-Back Propagation. Mehran University Research Journal of Engineering & Technology Volume 31 , Issue 3 Saba Baloch,Javed Ali Baloch,Mukhtiar Ali Unar Abstract In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference) to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks). The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.

Article Information

Channel Equalization Using Multilayer Perceptron Networks

Keywords: Blind Channel Equalization, Neural Networks, Noisy Signal, Multi Layer Perceptron, Error-Back Propagation.

Mehran University Research Journal of Engineering & Technology

Volume 31 , Issue 3

Saba Baloch,Javed Ali Baloch,Mukhtiar Ali Unar

Abstract

In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference) to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks). The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.