Superimposed Pilots based Adaptive Time-Selective Channel Estimation in MU-MIMO Systems

Abstract

This work proposes symbol and block level adaptive channel estimation schemes, based on the least mean squares (LMS) and block-LMS (BLMS) approaches, respectively, for multiuser-MIMO (MU-MIMO) systems. The proposed schemes do not require knowledge of the first and second order statistics of the time-varying MU-MIMO channel, while also having a lower computational complexity in comparison to the Kalman filter based channel estimation approaches present in the existing literature. Another important aspect of the proposed MU-MIMO framework is that channel estimation is carried out at the base station (BS), which simplifies the receiver architecture. Analytical expressions are derived for the error covariance matrix at each time instant and the asymptotic mean square error (MSE) of the proposed LMS and BLMS frameworks. Further, a superimposed pilot (SIP) framework for MU-MIMO channel estimation been developed, which transmits data symbols to a group of selected users during the training phase, thus leading to a significant improvement in the sum-rate performance. Simulation results are presented to demonstrate the improved sum-rate and MSE performance of the proposed schemes and also to verify the analytical results.