On the application of feed forward neural networks to channel equalization

Abstract

The application of feedforward neural networks to adaptive channel equalization is examined. The Rummler channel model is used for modeling the digital microwave radio channel. In applying neural networks to the channel equalization problem, complex neurons in the neural network are used. This allows for a frequency interpretation of the weights of the neurons in the first hidden layer. This channel model allows examination of binary signaling in two dimensions, (4-quadrature amplitude modulation, or QAM), and higher-level signaling as well, (16-QAM). Results show that while neural nets provide a significant performance increase in the case of binary signaling in two dimensions (4-QAM), this performance is not reflected in the results for the higher-level signaling schemes. In this case the neural net equalizer performance tends to parallel that of the linear transversal equalizer.<>