Channel Equalization Using Multilayer Perceptron Networks
This addresses channel distortion issues in digital communication systems, but it is incremental as it applies an existing neural network method to a known problem.
The paper tackled the problem of inter-symbol interference in digital communication systems by using a multilayer perceptron neural network for blind channel equalization, resulting in a visible reduction in noise content.
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.