Chaotic convergence of the decision-directed blind equalization algorithm

Classically, adaptive equalization algorithms are analyzed in terms of two possible steady state behaviors: convergence to a fixed point and divergence to infinity. This twofold scenario suits well the modus operandi of linear supervised algorithms, but can be rather restrictive when unsupervised methods are considered, as their intrinsic use of higher-order statistics gives rise to nonlinear update expressions. In this work, we show, using different analytical tools belonging to dynamic system theory, that one of the most emblematic and studied unsupervised approaches – the decision-directed algorithm – is potentially capable of presenting behaviors, like convergence to limit-cycles and chaos, that transcend the aforementioned dichotomy. These results also indicate theoretical possibilities concerning step-size selection and initialization.