Adaptive soft-constraint satisfaction (SCS) algorithms for fractionally-spaced blind equalizers

Constant modulus algorithms based on a deterministic error criterion are presented. Soft-constraint satisfaction methods yield a general family of blind equalization algorithms employing nonlinear functions of the equalizer output which must satisfy certain conditions. The algorithms are also extended to cover fractionally-spaced blind equalization. A normalization factor which appears as a result of the deterministic formulation of the problem helps the blind equalizer improve its performance. Also, the family supports a wide range of nonlinear functions. Extensive simulations are presented to reveal convergence characteristics which also include signals from the signal processing information base (SPIB)