New criteria for blind equalization based on PDF fitting

In this paper, we address M-QAM blind equalization based on information theoretic criteria. We propose two new cost functions that force the probability density functions (pdf) at the equalizer output to match the known constellation pdf. They involve kernel pdf approximation. The kernel bandwidth of a Parzen estimator is updated during iterations to improve the convergence speed and to decrease the residual error of the algorithms. Unlike related existing techniques, the new algorithms measure the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output separately. We show performance and complexity gain against the CMA, the most popular blind equalization technique, and classical pdf fitting approaches.