Semi‐blind fast equalization of QAM channels using concurrent gradient‐Newton CMA and soft decision‐directed scheme

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. Copyright © 2009 John Wiley & Sons, Ltd.