A convex semi-blind cost function for equalization in short burst communications

In short burst wireless communications, a training sequence is incorporated in each burst for the receiver to adjust the equalizer coefficients. However, when the amount of training symbols is less than the spatial-temporal equalizer tap weights, conventional least-square technique may not provide good MSE performance. Blind methods, on the other hand, may not achieve equalization in a short burst. A regularized semi-blind algorithm was proposed previously by Kuzminskiy et al. to overcome this problem but local minima exist in the algorithm. A convex cost with training symbols as the equalizer constraint is proposed in this paper to avoid cost-dependent local minima. Furthermore, comparison with the regularized semi-blind algorithm suggests that the proposed algorithm achieves a lower MSE performance in the case of nonconstant modulus signals such as 16-QAM signals.