Name
Abstract | ||
|---|---|---|
In this paper we have studied adaptive equalization in the GSM (Global System for Mobile communications) environment using radial basis function (RBF) networks. Equalization is here considered as a classification problem, where the idea is to map the received complex-valued signal into desired binary values using RBF network equalizer. Results prove that the RBF network provides very good bit error rates with acceptable computational complexity. Performance comparisons are made to a linear equalizer, a multilayer perceptron (MLP) network equalizer and to a Viterbi equalizer. |
Year | Venue | Keywords |
|---|---|---|
2003 | ESANN | multilayer perceptron,radial basis function,adaptive equalizer,bit error rate,radial basis function network,computational complexity |
Field | DocType | Citations |
Radial basis function network,GSM,Radial basis function,Equalization (audio),Computer science,Adaptive equalizer,Multilayer perceptron,Artificial intelligence,Viterbi algorithm,Machine learning,Computational complexity theory | Conference | 1 |
PageRank | References | Authors |
0.43 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arto Kantsila | 1 | 1 | 0.77 |
| M Lehtokangas | 2 | 158 | 21.87 |
| Jukka Saarinen | 3 | 264 | 46.21 |