Blind equalization of a noisy channel by linear neural network

Area of Science:

• Digital Communications
• Neural Networks
• Signal Processing

Background:

• Blind equalization is crucial for recovering transmitted signals distorted by unknown channels.
• Existing methods often require complex algorithms or training data.
• Digital communication systems face challenges from channel impairments like multipath fading.

Purpose of the Study:

• To introduce a new neural network-based approach for blind equalization.
• To establish necessary and sufficient conditions for effective blind equalization.
• To demonstrate the practical implementation and performance of the proposed method.

Main Methods:

• A two-layer linear neural network architecture is proposed.
• The hidden layer performs signal whitening.
• A stochastic approximate learning algorithm is utilized for network training.
• The output layer directly estimates source symbols.

Main Results:

• The proposed neural network effectively performs blind equalization.
• Simulation results validate the approach for a three-ray multipath channel.
• The method achieves accurate estimation of source symbols without prior channel knowledge.

Conclusions:

• The introduced neural approach offers an efficient solution for blind equalization.
• The two-layer linear network provides a practical implementation.
• This technique shows significant potential for improving digital communication reliability in challenging channel conditions.