Equalization of satellite UMTS channels using neural network devices
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 2563-2566 vol.5
- https://doi.org/10.1109/icassp.1999.760654
Abstract
The presence of nonlinear devices in several communication channels, such as satellite channels, causes distortions of the transmitted signal. These distortions are more severe for non-constant envelope modulations such as 16-QAM. Over the last years neural networks (NN) have emerged as competitive tools for linear and nonlinear channel equalization. However, their main drawback is often slow convergence speed which results in poor tracking capabilities. The present paper combines simple NN structures with conventional equalizers. The NN techniques are shown to efficiently approximate the optimal decision boundaries which results in good symbol error rate (SER) performance. The paper gives simulation examples (in the context of satellite mobile channels) and compares neural network approaches to classical equalization techniques.Keywords
This publication has 9 references indexed in Scilit:
- Applications of neural networks to digital communications – a surveySignal Processing, 2000
- Neural network modeling and identification of nonlinear channels with memory: algorithms, applications, and analytic modelsIEEE Transactions on Signal Processing, 1998
- Engineering applications of the self-organizing mapProceedings of the IEEE, 1996
- Schemes for equalisation of communication channels with nonlinear impairmentsIEE Proceedings - Communications, 1995
- Complex-valued radial basis function network, Part II: Application to digital communications channel equalisationSignal Processing, 1994
- Complex-valued radial basic function network, Part I: Network architecture and learning algorithmsSignal Processing, 1994
- Adaptive equalization of finite non-linear channels using multilayer perceptronsSignal Processing, 1990
- The multilayer perceptron as an approximation to a Bayes optimal discriminant functionIEEE Transactions on Neural Networks, 1990
- Frequency-Independent and Frequency-Dependent Nonlinear Models of TWT AmplifiersIEEE Transactions on Communications, 1981