This contribution considers semi-blind adaptive equalization for communication systems that employ high-throughput quadrature

amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension

of the equalizer, are first utilized to provide a rough initial least-squares estimate of the equalizer’s weight vector. A

novel gradient-Newton concurrent constant modulus algorithm and soft decision-directed scheme are then applied to adapt

the equalizer. The proposed semi-blind adaptive algorithm is capable of converging fast and accurately to the optimal

minimum mean-square error equalization solution. Simulation results obtained demonstrate that the convergence speed of this

semi-blind adaptive algorithm is close to that of the training-based recursive least-square algorithm