Blind Adaptive Multiple-Input Decision-Feedback Equalizer with a Self-Optimized Configuration

This paper introduces a novel blind adaptive multiple-input decision-feedback equalizer (MI-DFE) that is basically characterized by its ability to self-optimize its configuration, in terms of both structure and criteria, according to the severity of the transmission medium. In the first running mode, the novel equalizer is recursive, linear, and "blindly" adapted by criteria leading to a solution closely related to the minimum mean-square-error solution. In the second running mode, it becomes the conventional MI-DFE. From the viewpoints of both robustness and spectral efficiency, this equalizer proves to be very attractive since it avoids pathological behaviors, often encountered with the conventional trained MI-DFE, while requiring no training sequence. Furthermore, its very high speed of convergence renders it competitive in various standard applications, even in the case of burst mode transmission systems. Finally, the novel blind MI-DFE has been successfully tested on underwater acoustic communications signals, in a very severe context. The results are clearly convincing.