Equalization with decision delay estimation using recurrent neural networks
Advances in Engineering Software - Advanced algorithms and architectures for signal processing
Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel
Applied Soft Computing
Equalization with decision delay estimation using recurrent neural networks
Advances in Engineering Software - Advanced algorithms and architectures for signal processing
Blind equalization based on neural network under LS criterion by gradient iteration algorithm
ICNC'09 Proceedings of the 5th international conference on Natural computation
Conditional fuzzy clustering for blind channel equalization
Applied Soft Computing
| Hi-index | 35.68 |
In this paper, the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector regression (SVR) framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then, an iterative reweighted procedure is proposed to blindly restore the CM property. The technique is suitable for real and complex modulations, and it can also be generalized to nonlinear blind equalization using kernel functions. We present simulation examples showing that linear and nonlinear blind SV equalizers offer better performance than cumulant-based techniques, mainly in applications when only a small number of data samples is available, such as in packet-based transmission over fast fading channels.