Reduced complexity channel estimation method for multi input multi output-orthogonal frequency division multiplexing systems by subspace tracking

In multi input multi output orthogonal frequency division multiplexing systems, a group of low complexity subspace based time domain channel estimation methods are studied. These methods are based on parametric channel model, where the response of the channel is considered as a collection of sparse propagation paths. Considering the channel correlation matrix, translate estimation of channel parameters into an unconstrained minimized problem. To solve this problem, subspace tracking based Kalman filter method is proposed, which employs the constant subspace to construct state equation and measurement equation. The Least Mean Square and Recursive Least Square algorithms are applied and evaluated. These methods represent a group of low complexity subspace schemes. The approach can be extended to multi carrier code-division multiple-access systems. The simulation results prove that the Kalman filter method in time domain channel estimation can track faster fading channel, and is more accurate with low complexity.