Performance comparison of supervised and unsupervised/blind equalization algorithms for QAM transmitted constellations

The Concept of Adaptive Equalization is to counter Intersymbol Interference (ISI) which severely degrades the performance of a Communication System. Adaptive Equalization can be classified into Supervised/Trained Equalization and Unsupervised/Blind Equalization. Supervised Equalization is carried out with the aid of a training sequence and Blind Equalization is done without the aid of a training sequence. Godard\´s Constant Modulus Algorithm (CMA) was the very first blind equalization algorithm that could be used in two dimensional digital communication systems. It requires the knowledge of statistical properties of the transmitted data symbols to perform channel equalization. Picchi and Prati by modifying the standard Decision-Directed Algorithm proposed a new Blind Equalization Algorithm known as "Stop-and-Go" Decision-Directed Algorithm. This paper compares the performance of Blind Equalization algorithms: CMA and Stop-and-Go. Simulation results indicate faster convergence rate of Stop-and-Go than CMA for 16 and 64 Quadrature Amplitude Modulated (QAM) transmitted constellations. The results also indicate negligible difference between the MSE\´s of Stop-and-Go after convergence and supervized equalizer.