MIMO Linear Equalization With an$H^infty$Criterion

In this paper, we study the problem of linearly equalizing the multiple-input multiple-output (MIMO) communications channels from an$H^infty$point of view.$H^infty$estimation theory has been recently introduced as a method for designing filters that have acceptable performance in the face of model uncertainty and lack of statistical information on the exogenous signals. In this paper, we obtain a closed-form solution to the square MIMO linear$H^infty$equalization problem and parameterize all possible$H^infty$-optimal equalizers. In particular, we show that, for minimum phase channels, a scaled version of the zero-forcing equalizer is$H^infty$-optimal. The results also indicate an interesting dichotomy between minimum phase and nonminimum phase channels: for minimum phase channels the best causal equalizer performs as well as the best noncausal equalizer, whereas for nonminimum phase channels, causal equalizers cannot reduce the estimation error bounds from their a priori values. Our analysis also suggests certain remedies in the nonminimum phase case, namely, allowing for finite delay, oversampling, or using multiple sensors. For example, we show that$H^infty$equalization of nonminimum phase channels requires a time delay of at least$l$units, where$l$is the number of nonminimum phase zeros of the channel.