A blind decision feedback equalizer incorporating fixed lag smoothing

A new type of blind decision feedback equalizer (DFE) incorporating fixed l ag smoothing is developed in this paper. The structure is motivated by the fact that if we make full use of the dependence of the observed data on a g iven transmitted symbol, delayed decisions may produce better estimates of that symbol. To this end, we use a hidden Markov model (HMM) suboptimal for mulation that offers a good tradeoff between computational complexity and b it error rate (BER) performance. The proposed equalizer also provides estim ates of the channel coefficients and operates adaptively (so that it can ad apt to a fading channel for instance) by means of an online version of the expectation maximization (EM) algorithm. The resulting equalizer structure takes the form of a linear feedback system including a quantizer, and hence , it is easily implemented. In fact, because of its feedback structure, the proposed equalizer shows some similarities with the well-known DFE, A full theoretical analysis of the initial version of the algorithm is not availa ble, but a characterization of a simplified version is provided. We demonst rate that compared to the zero-forcing DFE (ZF-DFE), the algorithm yields m any improvements. A large range of simulations on finite impulse response ( FIR) channels and on typical fading GSM channel models illustrate the poten tial of the proposed equalizer.