  As several previous works have pointed out, the evolution of the wireless
channels in multiple input multiple output systems can be advantageously
modeled as an autoregressive process. Therefore, estimating the coefficients,
and, in particular, the state transition matrix of this autoregressive process
is a key to accurate channel estimation, tracking, and prediction in fast
fading environments. In this paper we assume a time varying spatially
uncorrelated channel, which is approximately the case with proper antenna
spacing at the base station in rich scattering environments. We propose a
method for autoregressive parameter estimation for the single input multiple
output (SIMO) channel. We show an almost sure convergence of the estimated
coefficients to the true autoregressive coefficients in large dimensions. We
apply the proposed method to SIMO channel tracking.
