Performance analysis of a RLS-based MLP-DFE in time-invariant and time-varying channels
Digital Signal Processing
GBF Trained Neuro-fuzzy Equalizer for Time Varying Channels
International Journal of Applied Evolutionary Computation
| Hi-index | 35.68 |
The problem of reconstructing digital signals which have been passed through a dispersive channel and corrupted with additive noise is discussed. The problems encountered by linear equalizers under adverse conditions on the signal-to-noise ratio and channel phase are described. By considering the equalization problem as a geometric classification problem the authors demonstrate how these difficulties can be overcome by utilizing nonlinear classifiers as channel equalizers. The manner in which neural networks can be utilized as adaptive channel equalizers is described, and simulation results which suggest that the neural network equalizers offer a performance which exceeds that of the linear structures, particularly in the high-noise environment, are presented