| We address the problems of channel estimation and optimal training sequence design for multiple-input and multiple-output (MIMO) systems over flat fading channels in the presence of colored interference. In practice, information of the unknown channel parameters is often obtained by sending known training symbols to the receiver. During the training period, we obtain the estimates of the channel parameters based on the received training block. This method is called training based channel estimation. In order to estimate unknown channel parameters, we employ two different channel estimators---the best linear unbiased estimator (BLUE) and Bayesian channel estimator. We consider the BLUE for the case where there is a single interferer with the deterministic channel assumption. We consider the Bayesian channel estimator for the case where there are multiple interferers with the assumption of random channels. We note that the mean square error (MSEs) of the channel estimators are dependent on the choice of the training sequence set. Hence we determine the optimal training sequence set that can minimize the MSEs of the channel estimators under a total transmit power constraint. In order to obtain the advantage of the optimal training sequence design, long-term statistics of the interference correlation are needed at the transmitter. Hence this information needs to be estimated at the receiver and fed back to the transmitter. It is desirable that if we can reduce the estimation error of the short-term channel fading parameters by using a minimal amount of information that is fed back from the receiver. We develop such a feedback strategy to design an approximate optimal training sequence set in this work. |
