Space-time channel estimation and soft detection in time-varying multiaccess channels (Q1853777)

This paper introduces an iterative space-time soft estimator (ISSE) that performs joint channel estimation and soft data detection in time-varying multiple-input multiple-output channels. The ISSE alternates channel estimation, taking explicitly into account the channel variability, and soft data detection. We suggest to implement the latter stage as a decision feedback (DF) structure consisting of two linear minimum mean square error (MMSE) matrix filters with an intercalated threshold detector. DF schemes are known to provide an appealing trade-off between performance and computational complexity. The main contribution of the paper is the channel estimation scheme. We derive a time-varying sequence of linear filters for channel estimation according to the MMSE principle. These filters depend both on the data and the second-order statistics of the channel. Hence, we introduce a scalar approximation of the channel autocovariance function that relies on the statistical homogeneity of the multipath scattering. Under this assumption, we also design a globally convergent block-adaptive algorithm to estimate and track the second-order statistics of a stationary channel. The robustness of the proposed approach when either the homogeneity or the stationarity hypotheses do not hold is shown through computer simulations.