TITLE:
Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters
AUTHORS:
Tiago Mota, Jorgean Leal, Antônio Lima
KEYWORDS:
Decision Feedback Equalizers; Levenberg-Marquardt Algorithm; Pulse Shaping; Recurrent Neural Networks
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.7 No.2,
February
8,
2014
ABSTRACT:
Artificial Neural Network (ANN) equalizers have been successfully
applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced
by linear or nonlinear communication channels. The ANN architecture is chosen
according to the type of ISI produced by fixed, fast or slow fading channels. In
this work, we propose a combination of two techniques in order to minimize ISI
yield by fast fading channels, i.e.,
pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used
to update the synaptic weights of an ANN comprise only by two recurrent
perceptrons. The proposed system outperformed more complex structures such as
those based on Kalman filtering approach.