The Effect of Training-Based Channel Estimation on the Capacity of Closed-Loop MIMO Systems With Imperfect CSI Feedback

In this paper, the authors study the influence of training-based channel estimation on the information-theoretic capacity of closed-loop MIMO systems with imperfect Channel State Information (CSI) feedback. First, a lower bound on capacity is formulated as a function of various parameters such as received signal-to-noise ratio, number of training symbols, feedback delay time, and the feedback noise variance. Next, they maximize the bound by obtaining the optimal allocation of power for training and data along with the optimal training interval. Fair comparison, through simulation, is also made with the maximized capacity of an open-loop MIMO system.