MIMO Equalization With State-Space Channel Models

State-space models are proposed to represent multiple-input multiple-output (MIMO) frequency-selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel. We develop a simple framework under which the equalizers for state-space channel models can be designed using the existing methods for designing equalizers for finite impulse response (FIR) models. In particular, a MIMO minimum mean-squared error decision feedback equalizer is developed for state-space models. When only estimates of the channel are available to the receiver, the equalization performance is affected by the channel estimation accuracy. Because reduced-order state-space models can provide lower H ₂ channel estimation error than reduced-length FIR models, state-space based equalizers typically exhibit significantly smaller symbol error rate than FIR-based ones. Thus, state-space channel models can be a more robust choice than FIR models in the presence of model order selection error. Numerical simulation also shows that adaptive state-space based receivers have slower but comparable convergence rates compared to FIR-based adaptive equalizers.