Adaptive equalization for PAM and QAM signals with neural networks
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10586393,p. 496-500
- https://doi.org/10.1109/acssc.1991.186499
Abstract
The authors investigate the application of neural networks to adaptive and blind equalization problems. The purpose is twofold: (1) to introduce a new realization structure of a multilayer perceptron (MLP) with a backpropagation training algorithm and show that it works well for both PAM and quadrature amplitude modulation (QAM) signals of any constellation size, and (2) to demonstrate the performance of self-organizing maps (SOMs) as blind equalizers and establish that they are simply not powerful enough for this problem, especially when the intersymbol interference is large. A new MLP structure for adaptive equalization of PAM and QAM signals is described and its performance, along with the simulation results of SOMs as blind equalizers, is demonstrated.Keywords
This publication has 3 references indexed in Scilit:
- The application of nonlinear structures to the reconstruction of binary signalsIEEE Transactions on Signal Processing, 1991
- Adaptive equalization of finite non-linear channels using multilayer perceptronsSignal Processing, 1990
- Combining linear equalization and self-organizing adaptation in dynamic discrete-signal detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990