An adaptive blind signal separation filter is proposed using a risk-sensitive criterion framework. This criterion adopts an exponential type function. Hence, the proposed criterion varies the consideration weight of an adaptation quantity depending on errors in the estimates: the adaptation is accelerated when the estimation error is large, and unnecessary acceleration of the adaptation does not occur close to convergence. In addition, since the algorithm derivation process relates to an H filtering, the derived algorithm has robustness to perturbations or estimation errors. Hence, this method converges faster than conventional least squares methods. Such effectiveness of the new algorithm is demonstrated by simulation.

In this paper, we discuss the applicability of H filtering algorithm for a decision feedback equalizer (DFE) in mobile communications environments. A comparison of bit error rate (BER) performance between a H2 filtering (recursive least squares) algorithm-based and an a priori H filtering algorithm-based fractionally-tap-spaced DFEs in various fading channels is presented. Against, our results are rather pessimistic of the H equalizer, namely, as compared with the H2 equalizer, at most the same or a little better BER performance of the H equalizer is obtained only when the ratio of the average received energy per bit to the white noise power spectral density and the normalized fading rate are large enough, especially in Rician fading channels. Moreover, the H equalizer has the problems of how to choose an appropriate prescribed positive value and computational complexity, therefore it may not be considered as an attractive alternative to the H2 equalizer for wireless communications systems.

In this letter, blind adaptive H multiuser detection is developed by employing a generalized sidelobe canceler (GSC) with and without subweight partition scheme. It is shown that the adaptive H algorithm with subweight approach has the advantages of fast convergence speed, insensitivity of dynamic estimate error, and suitability for arbitrary ambient noise over the conventional H and the RLS-based adaptive algorithms.