Adaptive Blind Equalization of QAM Transmitted Constellations across Linear Band-Limited Channel

ISI (Intersymbol Interference) severely degrades the performance of high speed digital communication systems. A popular technique for countering ISI is Adaptive Equalization. Blind/Unsupervized equalization is one class of adaptive equalization where initial training of equalizer’s weights is not feasible. Godard’s Constant Modulus Algorithm (CMA) was the very first blind equalization algorithm that could be applied for two dimensional communication systems. It requires the transmitted signal’s statistics to perform equalization. Picchi & Prati modified the standard decision-directed algorithm and proposed a new algorithm by the name Stop-and-Go. Wei Rao by simply converting the equalizer’s output proposed a modified version of the CMA. This paper compares the performance of blind equalization algorithms: CMA, Stop-and-Go decisiondirected algorithm and Wei Rao’s modified constant modulus algorithm for 16 and 64-QAM constellations transmitted across a linear band-limited channel. Simulation results indicate impulse response of the cascade of transmission channel and equalizer (after convergence) is similar to that of ideal transmission channel‘s impulse response. The results also show that stop-and-go outperforms the rest in terms of convergence rate and MSE.