The Achievable Rate of the Training-Based MIMO Systems over Time-Varying Fading Channels: A BCRB Perspective on Channel Tracking

Abstract

In this paper, we study the achievable rate of the training-based multi-input multi-output (MIMO) systems over time-varying flat fading channels modeled with the L-th order autoregressive, AR(L), process. Using the Bayesian Cramer-Rao lower bound (BCRB) to characterize the mean squared error of channel estimation, the achievable rate of the MIMO systems is investigated from the informationtheoretical perspective. The optimum lengths for the training and the data blocks are determined to maximize the achievable rate. Besides, by modeling the time-varying wireless fading channel with a proper AR(L) model for the specific normalized Doppler frequency, fdTs, the aforementioned results can be extended to characterize the achievable rate in a more realistic time-varying wireless fading channel.