Blind Equalization and Carrier Recovery Using a "Stop-and-Go" Decision-Directed Algorithm

We show that the standard decision-directed estimatedgradient adaptation algorithm for joint MSE equalization and carrier recovery, normally utilized in the open-eye condition, can be turned into an algorithm providing effective blind convergence in the MSE sense, usable in the closed-eye startup phase with no need of a known training sequence. This is obtained by means of a simple flag telling both the equalizer and the synchronizer whether the current output error with respect to the decided symbol is sufficiently reliable to be used. If not, adaptation is stopped for the current iteration. In the paper, this "stopand-go" decision-directed algorithm is presented for both linear and decision-feedback MSE complex equalizers with joint blind carrier recovery. Simulation results demonstrate the effectiveness of the proposed technique.