FPCM-Assisted Blind Channel Equalization of M-QAM Signals for Time-Varying Channels

Fuzzy c-means equalization is a blind channel equalization that has been shown to be successful in mitigating the effects of inter-symbol interference introduced by partial response modems and by dispersive channels. In this paper, we propose a fuzzy possibilistic c-means-assisted blind channel equalization scheme that is superior to the fuzzy c-means-assisted blind channel equalization by the capability of the FPCM to recognize and reject the inference of the seriously noisy data samples in the context of 4 quadrature amplitude modulation. Furthermore, an accumulative cluster centers scheme is proposed to properly increase the amount of the data involved into the clustering process. Simulation results show that the fuzzy possibilistic c-means blind channel equalization with accumulative cluster centers outperforms the fuzzy possibilistic c-means blind channel equalization without accumulative cluster centers under the time-varying channel.