  We address the problem of channel estimation for cyclic-prefix (CP)
Orthogonal Frequency Division Multiplexing (OFDM) systems. We model the channel
as a vector of unknown deterministic constants and hence, do not require prior
knowledge of the channel statistics. Since the mean-square error (MSE) is not
computable in practice, in such a scenario, we propose a novel technique using
Stein's lemma to obtain an unbiased estimate of the mean-square error, namely
the Stein's unbiased risk estimate (SURE). We obtain an estimate of the channel
from noisy observations using linear and nonlinear denoising functions, whose
parameters are chosen to minimize SURE. Based on computer simulations, we show
that using SURE-based channel estimate in equalization offers an improvement in
signal-to-noise ratio of around 2.25 dB over the maximum-likelihood channel
estimate, in practical channel scenarios, without assuming prior knowledge of
channel statistics.
