Adaptive channel estimation for trained MIMO-OFDM

In this paper, we present an adaptive Wiener filtering channel estimation method in the context of pilot symbols assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communications in low mobility environment. Optimal Wiener filtering requires known channel statistics. When there exists channel model mismatch, performance could degrade significantly. We propose to quantize the channel model space into a set of hypothesized models with different delay spread and uniform multipath arrivals with equal power. A quasi maximum likelihood detector is developed to choose the hypothesized model that is closest to the actual channel. The Wiener coefficients associated with the selected model are then used for channel estimation. Simulation results show that the proposed adaptive Wiener filtering achieves significant gain over a pessimistic Wiener design and very robust performance with respect to model variations.