State space modeling for MIMO wireless channels

State space models are proposed to represent MIMO frequency-selective wireless channels with the motivation of better model approximation performance and more parsimonious parametrization. We study the MIMO channel approximation error as a function of model order and show that state-space models possess improved performance compared to more standard FIR models. A recursive algorithm, based on the subspace system identification methods, to estimate the state-space channel model using training data is presented. When compared to the FIR-based recursive least squares algorithm, the state-space-based channel estimator shows the ability of providing low-order models of high-quality channel approximation, while preserving comparable convergence rate.