Linear-prediction whitening with convex combining in constant modulus equalizers

Among the well known issues with CMA equalizers is that they can converge slowly. Some recent work indicates that pre-whitening with an adaptive LMS filter, configured for linear-prediction (LP-CMA), can cause the CMA equalizer to converge faster, for some poor channel cases. While this works well for a high-dispersion channel, when the technique is applied to less problematic channels, convergence can be slower than non-whitened CMA. In this paper, we apply convex combining to adaptively select between an LP-CMA and CMA with no pre-whitening.