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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.

In this paper, we describe an efficient approach to overcome the need for matrix inversion required in most wired applications encountered in practice. In particular, MMSE equalization based on series expansion to approximate the matrix inversion is addressed. By adjusting a scaling factor, the series expansion is directly optimized according to a fixed order with respect to a system performance criterion. In comparison with previous approaches, the resulting equalizer enables improved BER performance according to a fixed order, in addition to low complexity without the need for a complicated eigenvalue calculation procedure. [ABSTRACT FROM PUBLISHER]