Channel Estimation for Precoded Mimo Systems

We consider a block fading frequency selective multi-input multioutput (MIMO) channel in additive white Gaussian noise (AWGN) The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. This form of precoding is referred to as aj%e pncoding. We derive the channel CramerRao bound (CRB) and we show that tr(CRB) can be lowered if we design the precoder and training such that the channel estimation through the training component is not affected by the precoded symbols. We propose a deterministic channel estimation algorithm which combines a second order blind estimator capitalized on the redundant precoding, with a standard linear estimator which exploits only training. Thc simulation result show a performance improvement over the least square (LS) which utilizes only training to obtain the channel estimate.