Abstract
This paper presents nonlinear blind equalization techniques using an adaptive bilinear polynomial filter. Two types of blind adaptive bilinear polynomial equalizers with reduced bilinear terms are proposed. One is a blind bilinear polynomial decision feedback equalizer using conventional constant modulus algorithm, which uses previous detected symbols. The other is a blind predictive constant modulus bilinear decision feedback equalizer, which uses error signals. In proposed equalizers, the input of the bilinear section is composed of the feedback inputs multiplied by not overall but middle parts of feedforward inputs. It can be seen by various simulations that proposed simplified blind bilinear equalizers perform better than the conventional blind decision feedback equalizer (DFE) and has almost the same performance as the conventional DFE using training sequences in a digital communication system.
| Original language | English |
|---|---|
| Pages | 2883-2888 |
| Number of pages | 6 |
| Publication status | Published - 2001 Jan 1 |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19 |
Other
| Other | International Joint Conference on Neural Networks (IJCNN'01) |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 01/7/15 → 01/7/19 |
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All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
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Equalization techniques using a simplified bilinear recursive polynomial perceptron with decision feedback. / Choi, S.; Ko, K.; Hong, D.
2001. 2883-2888 Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.Research output: Contribution to conference › Paper
TY - CONF
T1 - Equalization techniques using a simplified bilinear recursive polynomial perceptron with decision feedback
AU - Choi, S.
AU - Ko, K.
AU - Hong, D.
PY - 2001/1/1
Y1 - 2001/1/1
N2 - This paper presents nonlinear blind equalization techniques using an adaptive bilinear polynomial filter. Two types of blind adaptive bilinear polynomial equalizers with reduced bilinear terms are proposed. One is a blind bilinear polynomial decision feedback equalizer using conventional constant modulus algorithm, which uses previous detected symbols. The other is a blind predictive constant modulus bilinear decision feedback equalizer, which uses error signals. In proposed equalizers, the input of the bilinear section is composed of the feedback inputs multiplied by not overall but middle parts of feedforward inputs. It can be seen by various simulations that proposed simplified blind bilinear equalizers perform better than the conventional blind decision feedback equalizer (DFE) and has almost the same performance as the conventional DFE using training sequences in a digital communication system.
AB - This paper presents nonlinear blind equalization techniques using an adaptive bilinear polynomial filter. Two types of blind adaptive bilinear polynomial equalizers with reduced bilinear terms are proposed. One is a blind bilinear polynomial decision feedback equalizer using conventional constant modulus algorithm, which uses previous detected symbols. The other is a blind predictive constant modulus bilinear decision feedback equalizer, which uses error signals. In proposed equalizers, the input of the bilinear section is composed of the feedback inputs multiplied by not overall but middle parts of feedforward inputs. It can be seen by various simulations that proposed simplified blind bilinear equalizers perform better than the conventional blind decision feedback equalizer (DFE) and has almost the same performance as the conventional DFE using training sequences in a digital communication system.
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M3 - Paper
SP - 2883
EP - 2888
ER -